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  </channel><item rdf:about="https://arxiv.org/abs/2407.05991">
    <title>[2407.05991] Multi-Texture Synthesis through Signal Responsive Neural Cellular Automata</title>
    <dc:date>2026-02-21T20:35:23+00:00</dc:date>
    <link>https://arxiv.org/abs/2407.05991</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Neural Cellular Automata (NCA) have proven to be effective in a variety of fields, with numerous biologically inspired applications. One of the fields, in which NCAs perform well is the generation of textures, modelling global patterns from local interactions governed by uniform and coherent rules. This paper aims to enhance the usability of NCAs in texture synthesis by addressing a shortcoming of current NCA architectures for texture generation, which requires separately trained NCA for each individual texture. In this work, we train a single NCA for the evolution of multiple textures, based on individual examples. Our solution provides texture information in the state of each cell, in the form of an internally coded genomic signal, which enables the NCA to generate the expected texture. Such a neural cellular automaton not only maintains its regenerative capability but also allows for interpolation between learned textures and supports grafting techniques. This demonstrates the ability to edit generated textures and the potential for them to merge and coexist within the same automaton. We also address questions related to the influence of the genomic information and the cost function on the evolution of the NCA.
]]></description>
<dc:subject>pattern-formation generative-models generative-art neural-networks rather-interesting cellular-automata image-generation machine-learning note:distance-metric consider:code</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:da851ce41a6f/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2309.14564">
    <title>[2309.14564] Generative Escher Meshes</title>
    <dc:date>2024-10-27T23:25:16+00:00</dc:date>
    <link>https://arxiv.org/abs/2309.14564</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper proposes a fully-automatic, text-guided generative method for producing perfectly-repeating, periodic, tile-able 2D imagery, such as the one seen on floors, mosaics, ceramics, and the work of M.C. Escher. In contrast to square texture images that are seamless when tiled, our method generates non-square tilings which comprise solely of repeating copies of the same object. It achieves this by optimizing both geometry and texture of a 2D mesh, yielding a non-square tile in the shape and appearance of the desired object, with close to no additional background details, that can tile the plane without gaps nor overlaps. We enable optimization of the tile's shape by an unconstrained, differentiable parameterization of the space of all valid tileable meshes for given boundary conditions stemming from a symmetry group. Namely, we construct a differentiable family of linear systems derived from a 2D mesh-mapping technique - Orbifold Tutte Embedding - by considering the mesh's Laplacian matrix as differentiable parameters. We prove that the solution space of these linear systems is exactly all possible valid tiling configurations, thereby providing an end-to-end differentiable representation for the entire space of valid tiles. We render the textured mesh via a differentiable renderer, and leverage a pre-trained image diffusion model to induce a loss on the resulting image, updating the mesh's parameters so as to make its appearance match the text prompt. We show our method is able to produce plausible, appealing results, with non-trivial tiles, for a variety of different periodic tiling patterns.
]]></description>
<dc:subject>tiling Escher generative-art rather-interesting purdy-pitchers something-a-little-bit-weird-tho tesselation natural-language-processing image-processing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://arxiv.org/abs/2405.18029">
    <title>[2405.18029] Are Image Distributions Indistinguishable to Humans Indistinguishable to Classifiers?</title>
    <dc:date>2024-08-01T11:52:56+00:00</dc:date>
    <link>https://arxiv.org/abs/2405.18029</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The ultimate goal of generative models is to characterize the data distribution perfectly. For image generation, common metrics of visual quality (e.g., FID), and the truthlikeness of generated images to the human eyes seem to suggest that we are close to achieving it. However, through distribution classification tasks, we find that, in the eyes of classifiers parameterized by neural networks, the strongest diffusion models are still far from this goal. Specifically, classifiers consistently and effortlessly distinguish between real and generated images in various settings. Further, we observe an intriguing discrepancy: classifiers can identify differences between diffusion models with similar performance (e.g., U-ViT-H vs. DiT-XL), but struggle to differentiate between the smallest and largest models in the same family (e.g., EDM2-XS vs. EDM2-XXL), whereas humans exhibit the opposite tendency. As an explanation, our comprehensive empirical study suggests that, unlike humans, classifiers tend to classify images through edge and high-frequency components. We believe that our methodology can serve as a probe to understand how generative models work and inspire further thought on how existing models can be improved and how the abuse of such models can be prevented.
]]></description>
<dc:subject>generative-art machine-learning image-processing rather-interesting who-watches-the-watchbeings? feature-selection</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:17ff7e674344/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2109.05489">
    <title>[2109.05489] Illuminating Diverse Neural Cellular Automata for Level Generation</title>
    <dc:date>2024-05-25T12:49:45+00:00</dc:date>
    <link>https://arxiv.org/abs/2109.05489</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present a method of generating diverse collections of neural cellular automata (NCA) to design video game levels. While NCAs have so far only been trained via supervised learning, we present a quality diversity (QD) approach to generating a collection of NCA level generators. By framing the problem as a QD problem, our approach can train diverse level generators, whose output levels vary based on aesthetic or functional criteria. To efficiently generate NCAs, we train generators via Covariance Matrix Adaptation MAP-Elites (CMA-ME), a quality diversity algorithm which specializes in continuous search spaces. We apply our new method to generate level generators for several 2D tile-based games: a maze game, Sokoban, and Zelda. Our results show that CMA-ME can generate small NCAs that are diverse yet capable, often satisfying complex solvability criteria for deterministic agents. We compare against a Compositional Pattern-Producing Network (CPPN) baseline trained to produce diverse collections of generators and show that the NCA representation yields a better exploration of level-space.
]]></description>
<dc:subject>cellular-automata game-design generative-art rather-interesting neural-networks to-understand consider:genetic-programming to-simulate to-write-about consider:performance-measures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://arxiv.org/abs/2105.09492">
    <title>[2105.09492] DeepCAD: A Deep Generative Network for Computer-Aided Design Models</title>
    <dc:date>2022-03-17T10:50:36+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.09492</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Deep generative models of 3D shapes have received a great deal of research interest. Yet, almost all of them generate discrete shape representations, such as voxels, point clouds, and polygon meshes. We present the first 3D generative model for a drastically different shape representation --- describing a shape as a sequence of computer-aided design (CAD) operations. Unlike meshes and point clouds, CAD models encode the user creation process of 3D shapes, widely used in numerous industrial and engineering design tasks. However, the sequential and irregular structure of CAD operations poses significant challenges for existing 3D generative models. Drawing an analogy between CAD operations and natural language, we propose a CAD generative network based on the Transformer. We demonstrate the performance of our model for both shape autoencoding and random shape generation. To train our network, we create a new CAD dataset consisting of 178,238 models and their CAD construction sequences. We have made this dataset publicly available to promote future research on this topic.
]]></description>
<dc:subject>generative-models generative-art representation 3d genetic-programming would-be-simpler to-write-about to-reproduce consider:DSL consider:performance-measures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://fronkonstin.com/2020/03/26/watercolors/">
    <title>Watercolors | Fronkonstin</title>
    <dc:date>2022-01-02T13:23:13+00:00</dc:date>
    <link>https://fronkonstin.com/2020/03/26/watercolors/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Sometimes I think about the reasons why I spend so many time doing experiments and writing my discoveries in a blog. Even although the main reason to start this blog was some kind of vanity, today I have pretty clear why I still keep writing it: to keep my mind tuned. I really enjoy looking for ideas, learning new algorithms, figuring out the way to translate them into code and trying to discover new territories going a step further. I cannot imagine my life without coding. Many good times in the last years have been in front of my laptop listening music and drinking a beer. In these strange times, confined at house, coding has became in something more important. It keeps me ahead from the sad news and moves my mind to places where everything is quiet, friendly and perfect. Blogging is my therapy, my mindfulness.

]]></description>
<dc:subject>art experiment blogging generative-art to-write-about to-try</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:54480838a71c/</dc:identifier>
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</item>
<item rdf:about="https://rant-lang.org/">
    <title>Rant – The Procedural Generation Language</title>
    <dc:date>2022-01-01T14:48:46+00:00</dc:date>
    <link>https://rant-lang.org/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Procgen made easy.

Rant is a high-level language for templating and procedural generation. It enables you to easily create dynamic templates, dialogue, stories, name generators, test data, and much more with minimal code.

Rant is intuitive

Think of Rant as the opposite of Regex: just as a regular expression compares inputs to a pattern, Rant generates matching outputs from a pattern. If you understand one, you already understand the other!

Rant is concise

Rant's standard library provides built-in utilities for many common use cases, cutting down on the amount of boilerplate you need to write.

Even for more complex generation tasks, Rant has your back. With its powerful set of synchronization, branching, and generation tools, you can get results with far less code than conventional programming languages.

Rant is flexible

Rant is infinitely configurable for a wide range of use cases ranging from natural language generation to simple code templating. What you do with it is up to you!

]]></description>
<dc:subject>generative-art programming-language rather-interesting obsidian-plugin to-understand via:logista</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7fb43b685597/</dc:identifier>
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<item rdf:about="https://tedunderwood.com/2021/10/21/latent-spaces-of-culture/">
    <title>Mapping the latent spaces of culture – The Stone and the Shell</title>
    <dc:date>2021-11-07T13:57:46+00:00</dc:date>
    <link>https://tedunderwood.com/2021/10/21/latent-spaces-of-culture/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The technology at the center of this roundtable doesn’t yet have a consensus name. Some observers point to an architecture, the Transformer.[1] “On the Dangers of Stochastic Parrots” focuses on size and discusses “large language models.”[2] A paper from Stanford emphasizes applications: “foundation models” are those that can adapt “to a wide range of downstream tasks.”[3] Each definition identifies a different feature of recent research as the one that matters. To keep that question open, I’ll refer here to “deep neural models of language,” a looser category.

However we define them, neural models of language are already changing the way we search the web, write code, and even play games. Academics outside computer science urgently need to discuss their role. “On the Dangers of Stochastic Parrots” deserves credit for starting the discussion—especially since publication required tenacity and courage. I am honored to be part of an event exploring its significance for the humanities.]]></description>
<dc:subject>machine-learning generative-art linguistics deep-learning natural-language-processing rather-interesting digital-humanities you-got-aesthetics-in-my-criticism</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:60a991504b48/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:linguistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:natural-language-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:digital-humanities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:you-got-aesthetics-in-my-criticism"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.openhumanitiespress.org/books/titles/gathering-ecologies/">
    <title>Open Humanities Press– Gathering Ecologies</title>
    <dc:date>2021-09-03T13:32:01+00:00</dc:date>
    <link>http://www.openhumanitiespress.org/books/titles/gathering-ecologies/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[What might an interactive artwork look like that enabled greater expressive potential for all of the components of the event? How can we radically shift our idea of interactivity towards an ecological conception of the term, emphasising the generation of complex relation over the stability of objects and subjects? Gathering Ecologies explores this ethical and political shift in thinking, examining the creative potential of differential relations through key concepts from the philosophies of A.N. Whitehead, Gilbert Simondon and Michel Serres. Utilising detailed examinations of work by artists such as Lygia Clark, Rafael Lozano-Hemmer, Nathaniel Stern and Joyce Hinterding, the book discusses the creative potential of movement, perception and sensation, interfacing, sound and generative algorithmic design to tune an event towards the conditions of its own ecological emergence.

]]></description>
<dc:subject>interactivity philosophy criticism rather-interesting the-mangle-in-practice define-your-terms generative-art</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:18e474bded5e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interactivity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:criticism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:define-your-terms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.openhumanitiespress.org/books/titles/ai-art/">
    <title>Open Humanities Press– AI Art</title>
    <dc:date>2021-09-03T13:21:00+00:00</dc:date>
    <link>http://www.openhumanitiespress.org/books/titles/ai-art/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Can computers be creative? Is algorithmic art just a form of Candy Crush? Cutting through the smoke and mirrors surrounding computation, robotics and artificial intelligence, Joanna Zylinska argues that, to understand the promise of AI for the creative fields, we must not confine ourselves solely to the realm of aesthetics. Instead, we need to address the role and position of the human in the current technical setup – including the associated issues of labour, robotisation and, last but not least, extinction. Offering a critique of the socio-political underpinnings of AI, AI Art: Machine Visions and Warped Dreams raises poignant questions about the conditions of art making and creativity today.

The book critically examines artworks that use AI, be it in the form of visual style transfer, algorithmic experiment or critical commentary. It also engages with their predecessors, including robotic art and net art. AI Art includes a project from Zylinska’s own art practice titled ‘View from the Window’, which explores human and nonhuman forms of intelligence, perception and action. The book closes with speculation on future art – and on art’s future.


]]></description>
<dc:subject>generative-art philosophy-of-engineering rather-interesting to-read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a19c9cc30efa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://linescurvesspirals.blogspot.com/2021/03/l-tilings.html">
    <title>L Tilings</title>
    <dc:date>2021-08-01T12:29:23+00:00</dc:date>
    <link>http://linescurvesspirals.blogspot.com/2021/03/l-tilings.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[L triominoes are one of my favourite tiling shapes. I have written about them on my blog before and I return to them again and again. I played around with them last year during Annie Perkin's Math Art Challenge though I didn't spend as long on the idea as I would have liked to. 

]]></description>
<dc:subject>tiling generative-art mathematical-recreations rather-interesting consider:constraints</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:020087429edb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tiling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:constraints"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2107.05383">
    <title>[2107.05383] Not Quite 'Ask a Librarian': AI on the Nature, Value, and Future of LIS</title>
    <dc:date>2021-07-24T11:52:27+00:00</dc:date>
    <link>https://arxiv.org/abs/2107.05383</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[AI language models trained on Web data generate prose that reflects human knowledge and public sentiments, but can also contain novel insights and predictions. We asked the world's best language model, GPT-3, fifteen difficult questions about the nature, value, and future of library and information science (LIS), topics that receive perennial attention from LIS scholars. We present highlights from its 45 different responses, which range from platitudes and caricatures to interesting perspectives and worrisome visions of the future, thus providing an LIS-tailored demonstration of the current performance of AI language models. We also reflect on the viability of using AI to forecast or generate research ideas in this way today. Finally, we have shared the full response log online for readers to consider and evaluate for themselves.
]]></description>
<dc:subject>generative-art the-mangle-in-practice rather-odd amusing artificial-oracles</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:608f1019c246/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-odd"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:amusing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-oracles"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://alwaysalready.dreamwidth.org/5200.html">
    <title>alwaysalready | neural net poetry process</title>
    <dc:date>2021-05-22T12:45:49+00:00</dc:date>
    <link>https://alwaysalready.dreamwidth.org/5200.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[I was asked about my process for creating neural net poetry, and thought it would be fun to write it out. If you want to have a look at my stuff, you can get my zines here. Everything is free but donations are gratefully appreciated.

So I have two distinct ways of generating neural net poetry, really. Going to write this as a guide in case anyone else wants to give it a try.
]]></description>
<dc:subject>generative-art neural-networks natural-language-processing algorithms artisitic-process to-write-about literariness</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bf5699998d88/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:natural-language-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artisitic-process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:literariness"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://algorithmicbotany.org/papers/">
    <title>Algorithmic Botany: Publications</title>
    <dc:date>2021-05-20T11:23:22+00:00</dc:date>
    <link>http://algorithmicbotany.org/papers/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The following is a selection of the papers published by Dr. P. Prusinkiewicz and his students and colleagues. Report any problems to vlab@cpsc.ucalgary.ca.

]]></description>
<dc:subject>algorithms artificial-life generative-art rather-interesting bibliography to-write-about consider:animations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9255be81e8c4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bibliography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:animations"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://fronkonstin.com/2019/10/03/colonizing-franky/">
    <title>Colonizing Franky | Fronkonstin</title>
    <dc:date>2021-05-20T11:21:21+00:00</dc:date>
    <link>https://fronkonstin.com/2019/10/03/colonizing-franky/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[One of my favorite sites in the Internet is algorithmic botany . It’s always a source of inspiration for me. I recently discovered there the space colonization algorithm, concretely in this paper. Originally, the algorithm was developed to simulate leaf venation patterns as well as the branching structure of trees and it works by simulating the competition for space between growing veins (or branches). Given a initial set of attractor points (3.000 points in my case), and a initial node (also a point located randomly inside the picture) the algorithm performs the next steps iteratively:

]]></description>
<dc:subject>generative-art artificial-life swarms rather-interesting to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c97a917a8070/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:swarms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/beardicus/awesome-plotters">
    <title>Awesome Plotters</title>
    <dc:date>2021-02-06T22:47:40+00:00</dc:date>
    <link>https://github.com/beardicus/awesome-plotters</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[List of code and resources for plotter]]></description>
<dc:subject>plotter generative-art shopping wishful-thinking via:nelson</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cd9cbdc8dfd4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:plotter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:shopping"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wishful-thinking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:nelson"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://bit-player.org/2019/my-god-its-full-of-dots">
    <title>My God, It’s Full of Dots! | bit-player</title>
    <dc:date>2020-12-09T12:24:29+00:00</dc:date>
    <link>http://bit-player.org/2019/my-god-its-full-of-dots</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This scheme for filling space with randomly placed objects is the invention of John Shier, a physicist who worked for many years in the semiconductor industry and who has also taught at Normandale Community College near Minneapolis. He explains the method and the mathematics behind it in a recent book, Fractalize That! A Visual Essay on Statistical Geometry. (For bibliographic details see the links and references at the end of this essay.) I learned of Shier’s work from my friend Barry Cipra.
Shier hints at the strangeness of these doings by imagining a set of 100 round tiles in graduated sizes, with a total area approaching one square meter. He would give the tiles to a craftsman with these instructions:
]]></description>
<dc:subject>nonlinear-dynamics mathematical-recreations fractals rather-interesting dynamical-systems to-write-about to-simulate consider:inverse-problem generative-art algorithms constant-finding</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2c7d562b2cc2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fractals"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:inverse-problem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:constant-finding"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.johndcook.com/blog/2020/11/09/some-mathematical-art/">
    <title>Some mathematical art</title>
    <dc:date>2020-11-13T23:34:41+00:00</dc:date>
    <link>https://www.johndcook.com/blog/2020/11/09/some-mathematical-art/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This evening I ran across a paper on an unusual coordinate system that creates interesting graphs based from simple functions. It’s called “circular coordinates,” but this doesn’t mean polar coordinates; it’s more complicated than that. [1]

]]></description>
<dc:subject>mathematical-recreations art rather-interesting generative-art to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:24085da478d2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.josleys.com/show_gallery.php?galid=284">
    <title>Gallery : Voronoi diagrams</title>
    <dc:date>2020-09-30T14:41:19+00:00</dc:date>
    <link>http://www.josleys.com/show_gallery.php?galid=284</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Voronoi diagrams
Acknowledgments to Craig S. Kaplan , who introduced me
to ornamental Voronoi designs through his paper on the subject.

]]></description>
<dc:subject>tiling mathematical-recreations generative-art rather-interesting to-write-about to-animate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6091c358cd11/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tiling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-animate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mcdbooks.com/features/sourdough">
    <title>MCD | Making the Music of the Mazg</title>
    <dc:date>2020-05-02T10:41:19+00:00</dc:date>
    <link>https://www.mcdbooks.com/features/sourdough</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Here’s how it came to pass that the Sourdough audiobook is the first, as far as I know, to include, tucked into its human narration, a contribution from a creative machine.

In case you don’t know: audiobooks are amazing now! They’re the fastest-growing part of the publishing industry, and their production has kept pace with their popularity. Audiobooks circa-2017 aren’t flat recitations of the words on the page; they’re a genre all their own, often enhanced with material that’s not available anywhere else.

So, the Sourdough audiobook contains several short chapters I wrote expressly for its listeners. And, in addition, something stranger.

]]></description>
<dc:subject>neural-networks generative-art case-study rather-interesting to-write-about to-try consider:feature-discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:349edb7a4d05/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:case-study"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-try"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:feature-discovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://medium.com/processing-foundation/p5-js-1-0-is-here-b7267140753a">
    <title>p5.js 1.0 is Here! - Processing Foundation - Medium</title>
    <dc:date>2020-03-08T20:38:48+00:00</dc:date>
    <link>https://medium.com/processing-foundation/p5-js-1-0-is-here-b7267140753a</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Today we are excited to announce the 1.0 Release of p5.js! p5.js is a JavaScript library that aims to make creative expression and coding on the web accessible and inclusive for artists, designers, educators, and beginners. While it’s been nearly seven years since p5.js began, we intentionally embarked on the path to reach 1.0 a year ago when Kate Hollenbach worked on a first version of a roadmap for this. From there, the effort was led by Stalgia Grigg and Evelyn Masso, working with Lauren McCarthy, Cassie Tarakajian, Kenneth Lim, and the thousands of contributors from around the world that joined in working on everything including code, documentation, teaching, outreach, writing, art making, and more. Reflecting the p5.js project values, 1.0 is not just a code-based milestone, but one that is grounded by significant work on the documentation and community.
]]></description>
<dc:subject>processing javascript library generative-art to-use to-write-about consider:quil</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a97cbfd45995/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:javascript"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:library"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-use"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:quil"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://muircheartblog.wordpress.com/2019/06/07/does-art-compute/">
    <title>Does art compute? – Symptoms Of The Universe</title>
    <dc:date>2020-02-16T12:50:15+00:00</dc:date>
    <link>https://muircheartblog.wordpress.com/2019/06/07/does-art-compute/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[As Cory Simon explains so well in his “Voronoi cookies and the post office problem” post, the Voronoi algorithm is an easy-to-understand method in computational geometry, especially in two dimensions: take a point, join it up to its nearest neighbours, and get the perpendicular bisectors of those lines. The intersections of the bisectors define a Voronoi cell. If the points form an ordered mesh on the plane — as, for example, in the context of the atoms on a crystal plane in solid state physics — then the Voronoi cell is called a Wigner-Seitz unit cell. (As an undergrad, I didn’t realise that the Wigner-Seitz unit cells I studied in my solid state lectures were part of the much broader Voronoi class — another example of limiting thinking due to disciplinary boundaries.)

]]></description>
<dc:subject>unconventional-computing generative-art interdisciplinary artificial-life heavily-linked to-walk-from</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:49857fd3ab78/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:unconventional-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interdisciplinary"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:heavily-linked"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-walk-from"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://inconvergent.net/2019/colour-shift/">
    <title>Depth of Field with Colour Shift · inconvergent</title>
    <dc:date>2020-01-12T21:14:48+00:00</dc:date>
    <link>https://inconvergent.net/2019/colour-shift/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The last time I wrote, I described a relatively easy way to get a nice depth of field effect. Let's see how we can add a colour shift effect as well. I've tried to repeat most of the relevant information here, but you might want to read the previous post before you continue. 

]]></description>
<dc:subject>generative-art visualization algorithms animation programming to-simulate to-try</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e683918685e9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:animation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-try"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://twitter.com/daisystanton/status/1187604793046401024">
    <title>daisy stanton on Twitter: &quot;If you train an end-to-end text-to-speech model whose attention fails to align, you can get hilarious descents into speech madness like this: https://t.co/wB1XLJifxp. Sadly, @ericbattenberg has reduced the frequency that we'll g</title>
    <dc:date>2020-01-10T15:54:07+00:00</dc:date>
    <link>https://twitter.com/daisystanton/status/1187604793046401024</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[If you train an end-to-end text-to-speech model whose attention fails to align, you can get hilarious descents into speech madness like this: https://google.github.io/tacotron/publications/location_relative_attention/audio/00036.mp3…. Sadly, 
@ericbattenberg
 has reduced the frequency that we'll get to cry with laughter in our office over these...
]]></description>
<dc:subject>text-to-speech neural-networks generative-art madness speech-synthesis rather-interesting to-write-about what-was-I-going-to-say?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:143e3780775e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:text-to-speech"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:madness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:speech-synthesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:what-was-I-going-to-say?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://christophercarlson.com/portfolio/multi-scale-truchet-patterns/">
    <title>Multi-Scale Truchet Patterns – Christopher Carlson</title>
    <dc:date>2020-01-01T12:51:05+00:00</dc:date>
    <link>https://christophercarlson.com/portfolio/multi-scale-truchet-patterns/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Start with the set of “winged” tiles shown below. The content of a tile is shown within the dotted lines. “Wings” extend beyond the content area. (The gray background is not part of the tile, but is included so you can see the boundary of the white parts.) Successive tiles in the set are scaled by 1/2, and black and white are swapped at each step. The boundaries between black and white meet the dotted lines at 1/3 and 2/3:

]]></description>
<dc:subject>truchet-tiles generative-art rather-interesting tiling fractals to-write-about to-do</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7d0ba3555acb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:truchet-tiles"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tiling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fractals"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-do"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://wiki.geogebra.org/en/Reference:File_Format">
    <title>Reference:File Format - GeoGebra Manual</title>
    <dc:date>2019-07-23T20:28:49+00:00</dc:date>
    <link>https://wiki.geogebra.org/en/Reference:File_Format</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Modifying .ggb or .ggt files (namely the .xml files within them) is clearly a task for the most tech-savvy users of GeoGebra. Whether you want to touch the .xml because you want to modify something which can't be modified by GeoGebra at the moment, like the definition of a custom tool, or you want to trick GeoGebra or just experiment you should take some tips for your journey:

]]></description>
<dc:subject>GeoGebra project to-do to-understand XML generative-art</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a7dbe4738cff/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:GeoGebra"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:project"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-do"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:XML"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1904.01175">
    <title>[1904.01175] DeepLight: Learning Illumination for Unconstrained Mobile Mixed Reality</title>
    <dc:date>2019-06-23T11:29:23+00:00</dc:date>
    <link>https://arxiv.org/abs/1904.01175</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present a learning-based method to infer plausible high dynamic range (HDR), omnidirectional illumination given an unconstrained, low dynamic range (LDR) image from a mobile phone camera with a limited field of view (FOV). For training data, we collect videos of various reflective spheres placed within the camera's FOV, leaving most of the background unoccluded, leveraging that materials with diverse reflectance functions reveal different lighting cues in a single exposure. We train a deep neural network to regress from the LDR background image to HDR lighting by matching the LDR ground truth sphere images to those rendered with the predicted illumination using image-based relighting, which is differentiable. Our inference runs at interactive frame rates on a mobile device, enabling realistic rendering of virtual objects into real scenes for mobile mixed reality. Training on automatically exposed and white-balanced videos, we improve the realism of rendered objects compared to the state-of-the art methods for both indoor and outdoor scenes.
]]></description>
<dc:subject>augmented-reality deep-learning image-processing data-fusion rather-interesting generative-models generative-art algorithms to-write-about consider:performance-measures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1bb227741439/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:augmented-reality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-fusion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:performance-measures"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/curv3d/curv">
    <title>curv3d/curv: a language for making art using mathematics</title>
    <dc:date>2019-04-20T22:23:08+00:00</dc:date>
    <link>https://github.com/curv3d/curv</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Curv is a programming language for creating art using mathematics. It's a 2D and 3D geometric modelling tool that supports full colour, animation and 3D printing.

Features:

Curv is a simple, powerful, dynamically typed, pure functional programming language.
Curv is easy to use for beginners. It has a standard library of predefined geometric shapes, plus operators for transforming and combining shapes. These can be plugged together like Lego to make 2D and 3D models.
Coloured shapes are represented using Function Representation (F-Rep). They can be infinitely detailed, infinitely large, and any shape or colour pattern that can be described using mathematics can be represented exactly.
Curv exposes the full power of F-Rep programming to experts. The standard geometry library is written entirely in Curv. Many of the demos seen on shadertoy.com can be reproduced in Curv, using shorter, simpler programs. Experts can package techniques used on shadertoy as high level operations for use by beginners.
Rendering is GPU accelerated. Curv programs are compiled into fragment shaders which are executed on the GPU.
Curv can export meshes to STL, OBJ and X3D files for 3D printing. The X3D format supports full colour 3D printing (on Shapeways.com, at least). These meshes are defect free: watertight, manifold, with no self intersections, degenerate triangles, or flipped triangles.
]]></description>
<dc:subject>mathematical-recreations generative-art 3d programming-language visualization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8a958f27de30/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:3d"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:programming-language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:visualization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://escholarship.org/uc/item/1340j5h2">
    <title>Curating Simulated Storyworlds</title>
    <dc:date>2019-03-03T13:03:06+00:00</dc:date>
    <link>https://escholarship.org/uc/item/1340j5h2</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[There is a peculiar method in the area of procedural narrative called emergent narrative: instead of automatically inventing stories or deploying authored narrative content, a system simulates a storyworld out of which narrative may emerge from the happenstance of character activity in that world. It is the approach taken by some of the most successful works in the history of computational media (The Sims, Dwarf Fortress), but curiously also some of its most famous failures (Sheldon Klein's automatic novel writer, Tale-Spin). How has this been the case? To understand the successes, we might ask this essential question: what is the pleasure of emergent narrative? I contend that the form works more like nonfiction than fiction—emergent stories actually happen—and this produces a peculiar aesthetics that undergirds the appeal of its successful works. What then is the pain of emergent narrative? There is a ubiquitous tendency to misconstrue the raw transpiring of a simulation (or a trace of that unfolding) as being a narrative artifact, but such material will almost always lack story structure.

So, how can the pain of emergent narrative be alleviated while simultaneously maintaining the pleasure? This dissertation introduces a refined approach to the form, called curationist emergent narrative (or just 'curationism'), that aims to provide an answer to this question. Instead of treating the raw material of simulation as a story, in curationism that material is curated to construct an actual narrative artifact that is then mounted in a full-fledged media experience (to enable human encounter with the artifact). This recasts story generation as an act of recounting, rather than invention. I believe that curationism can also explain how both wild successes and phenomenal failures have entered the oeuvre of emergent narrative: in successful works, humans have taken on the burden of curating an ongoing simulation to construct a storied understanding of what has happened, while in the failures humans have not been willing to do the necessary curation. Without curation, actual stories cannot obtain in emergent narrative.

But what if a storyworld could curate itself? That is, can we build systems that automatically recount what has happened in simulated worlds? In the second half of this dissertation, I provide an autoethnography and a collection of case studies that recount my own personal (and collaborative) exploration of automatic curation over the course of the last six years. Here, I report the technical, intellectual, and media-centric contributions made by three simulation engines (World, Talk of the Town, Hennepin) and three second-order media experiences that are respectively driven by those engines (Diol/Diel/Dial, Bad News, Sheldon County). In total, this dissertation provides a loose history of emergent narrative, an apologetics of the form, a polemic against it, a holistic refinement (maintaining the pleasure while killing the pain), and reports on a series of artifacts that represent a gradual instantiation of that refinement. To my knowledge, this is the most extensive treatment of emergent narrative to yet appear.]]></description>
<dc:subject>generative-art narrative emergent-design rather-interesting to-understand games constraint-satisfaction the-mangle-in-practice to-read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:929c2da61c9e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:narrative"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:constraint-satisfaction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/Clojure2D/clojure2d">
    <title>Clojure2D/clojure2d: Java2D wrapper + creative coding supporting functions (based on Processing and openFrameworks)</title>
    <dc:date>2019-02-05T16:57:39+00:00</dc:date>
    <link>https://github.com/Clojure2D/clojure2d</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Clojure2D is a library supporting generative coding or glitching. It's based on Java2D directly. It's Clojure only, no ClojureScript version.]]></description>
<dc:subject>generative-art Clojure to-do graphics library glitch</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:da1444bbe7db/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Clojure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-do"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:graphics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:library"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:glitch"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1812.04948">
    <title>[1812.04948] A Style-Based Generator Architecture for Generative Adversarial Networks</title>
    <dc:date>2018-12-13T19:10:18+00:00</dc:date>
    <link>https://arxiv.org/abs/1812.04948</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.
]]></description>
<dc:subject>generative-art generative-models neural-networks multiscale very-impressive to-write-about consider:performance-measures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3f702abbfe67/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiscale"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:very-impressive"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:performance-measures"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1811.08759">
    <title>[1811.08759] Using AI to Design Stone Jewelry</title>
    <dc:date>2018-12-11T12:54:14+00:00</dc:date>
    <link>https://arxiv.org/abs/1811.08759</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Jewelry has been an integral part of human culture since ages. One of the most popular styles of jewelry is created by putting together precious and semi-precious stones in diverse patterns. While technology is finding its way in the production process of such jewelry, designing it remains a time-consuming and involved task. In this paper, we propose a unique approach using optimization methods coupled with machine learning techniques to generate novel stone jewelry designs at scale. Our evaluation shows that designs generated by our approach are highly likeable and visually appealing.
]]></description>
<dc:subject>generative-art design aesthetics rather-interesting performance-measure to-write-about user-centric-design</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:57e10cab6a2c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aesthetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:user-centric-design"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.youtube.com/watch?v=Ic_5gRVTQ_k">
    <title>&quot;Mapping Imaginary Cities&quot; by Mouse Reeve - YouTube</title>
    <dc:date>2018-12-09T11:54:22+00:00</dc:date>
    <link>https://www.youtube.com/watch?v=Ic_5gRVTQ_k</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[While the map is not the territory (to quote the semantician Alfred Korzybski), the map is still usually intended to correspond to one. But what about maps of nowhere at all? What can they represent and how can they be made?

Maps are a familiar part of daily life, with a deeply familiar and complex symbolic language, and a long history. They are also hugely varied in style and aesthetic, and often are works of art unto themselves. All this makes mapping a powerful creative tool for conveying ideas about a space, how it is used, and who inhabits it. But it also presents a mapmaker with what can feel like an overwhelming array of design choices and technical hurdles to overcome in order to create a generative map.

This talk will explore maps as a way to communicate about people and place in the context of fictional cities, and dive into algorithms and techniques for procedurally generating maps by building up topography, landscape, populations, and street plans.
Maps are a familiar part of daily life, with a deeply familiar and complex symbolic language, and a long history. They are also hugely varied in style and aesthetic, and often are works of art unto themselves. All this makes mapping a powerful creative tool for conveying ideas about a space, how it is used, and who inhabits it. But it also presents a mapmaker with what can feel like an overwhelming array of design choices and technical hurdles to overcome in order to create a generative map.

This talk will explore maps as a way to communicate about people and place in the context of fictional cities, and dive into algorithms and techniques for procedurally generating maps by building up topography, landscape, populations, and street plans.]]></description>
<dc:subject>video cartography strange-loop rather-interesting art generative-art generative-models the-mangle-in-practice learning-in-public algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4807d5a5897b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:video"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cartography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:strange-loop"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-in-public"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.prospectmagazine.co.uk/arts-and-books/the-oulipo-of-the-1980s-why-its-time-to-reappraise-the-humble-choose-your-own-adventure-book">
    <title>The Oulipo of the 1980s? Why it’s time to reappraise the humble Choose Your Own Adventure book | Prospect Magazine</title>
    <dc:date>2018-12-08T12:49:49+00:00</dc:date>
    <link>https://www.prospectmagazine.co.uk/arts-and-books/the-oulipo-of-the-1980s-why-its-time-to-reappraise-the-humble-choose-your-own-adventure-book</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The allure of nostalgia is powerful, especially in an uncertain, unstable age. Nostalgia is a soothing form of selective amnesia of how things actually were. However forward-thinking and ostensibly unsentimental we might be, there are very few of us who are not moved in some way by these jolts of recognition and the comforting, if illusory, thought that a golden age existed in the past when life was more certain and more stable.

With Generation X beginning to reach middle age in slow horrified disbelief, it’s little surprise that 1980s revivalisms are big business, from Stranger Things and Ready Player One to the recent Star Wars resurrection. A joyless cynic might see this trend as an example of a culture paralysed by conservatism, cowardice and infantilism.

Yet it’s hard to deny the involuntary memories evoked upon seeing pixelated graphics or hearing the shriek of a TIE fighter. The best of these revivals (Twin Peaks: The Return, Blade Runner 2049) offer startling new directions amidst the familiar ones, which recontextualize that which came before. These stories are reimagined, rather than repeated to diminishing effect. Others are shallower.

]]></description>
<dc:subject>oulipo parafiction literary-criticism generative-art user-centric-art rather-interesting nostalgia</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c14bb7b9b40f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:oulipo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parafiction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:literary-criticism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:user-centric-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nostalgia"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://towardsdatascience.com/neuralfunk-combining-deep-learning-with-sound-design-91935759d628">
    <title>NeuralFunk - Combining Deep Learning with Sound Design</title>
    <dc:date>2018-11-12T12:10:26+00:00</dc:date>
    <link>https://towardsdatascience.com/neuralfunk-combining-deep-learning-with-sound-design-91935759d628</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[NeuralFunk - Combining Deep Learning with Sound Design
Making a Track Entirely out of Samples Generated by Neural Networks]]></description>
<dc:subject>rather-interesting neural-networks generative-art learning-in-public the-mangle-in-practice to-write-about performance-measure</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:53dcda6a07b0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-in-public"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://twitter.com/alexjc/status/1054279796417552384">
    <title>Alex J. Champandard on Twitter: &quot;Neural approaches to style transfer struggle with certain types of art, e.g. crisp yet smooth brush-strokes 🖋️. It's likely a combination of factors, including using models pre-trained on natural images. 📷 In this </title>
    <dc:date>2018-11-01T09:36:17+00:00</dc:date>
    <link>https://twitter.com/alexjc/status/1054279796417552384</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Neural approaches to style transfer struggle with certain types of art, e.g. crisp yet smooth brush-strokes . It's likely a combination of factors, including using models pre-trained on natural images. ]]></description>
<dc:subject>generative-art neural-networks rather-interesting aesthetics the-mangle-in-practice to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9970c64afdce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aesthetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://fibonaccisusan.com/2017/12/12/kelsey-brookes-at-the-jacob-lewis-gallery/">
    <title>Kelsey Brookes at the Jacob Lewis Gallery | fibonaccisusan</title>
    <dc:date>2018-10-21T12:44:20+00:00</dc:date>
    <link>https://fibonaccisusan.com/2017/12/12/kelsey-brookes-at-the-jacob-lewis-gallery/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Kelsey Brookes current solo exhibition at the Jacob Lewis gallery is titled ” The Mathematics Underlying Art”. I was so happy to see that the Fibonacci Sequence is a major theme for these large scale paintings. Each square canvas is divided into thirteen (13 is a Fibonacci Number) wedges radiating from the center point. Then dots are made along each dividing line at intervals that correspond to the Fibonacci Sequence.
]]></description>
<dc:subject>generative-art rather-interesting to-follow</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fb0114d1c11d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-follow"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.3ammagazine.com/3am/the-policemans-beard-is-algorithmically-constructed/">
    <title>The Policeman’s Beard is Algorithmically Constructed - 3:AM Magazine</title>
    <dc:date>2018-10-14T12:33:05+00:00</dc:date>
    <link>https://www.3ammagazine.com/3am/the-policemans-beard-is-algorithmically-constructed/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The Policeman’s Beard is an aggressively egotistical book. Measuring 22.6 x 20.3 x 1.5 centimetres, it dwarfs many of its neighbours on the shelf. Its paper cover is bright red, with a doctored photograph of a man who occupies a sturdy frame as he glares at prospective readers. The book begs to be handled, while at the same time warning readers to approach with caution.

And caution is indeed warranted, for The Policeman’s Beard and Racter are puzzling. In some ways, Racter does adhere to the modern conception of authorship. As with any human writer, Racter’s code interacts with a world—albeit a limited world that has been consciously created by its programmers—as a source of information, and remixes content to create unique texts. Yet Racter is rigid, using fixed functions to complete a particular task. The program cannot interpret that which it produces and, indeed, not until a human interprets Racter’s output can it be assigned any cultural value.

]]></description>
<dc:subject>generative-art nanohistory natural-language-processing I-remember-it-well the-mangle-in-practice to-write-about literary-criticism computational-criticism performative-writing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:da34b369ea28/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nanohistory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:natural-language-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:I-remember-it-well"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:literary-criticism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-criticism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performative-writing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.artnome.com/news/2018/8/8/why-love-generative-art">
    <title>Why Love Generative Art? — Artnome</title>
    <dc:date>2018-10-04T10:47:01+00:00</dc:date>
    <link>https://www.artnome.com/news/2018/8/8/why-love-generative-art</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Over the last 50 years, our world has turned digital at breakneck speed. No art form has captured this transitional time period - our time period - better than generative art. Generative art takes full advantage of everything that computing has to offer, producing elegant and compelling artworks that extend the same principles and goals artists have pursued from the inception of modern art.

]]></description>
<dc:subject>generative-art criticism have-read to-write-about aesthetics philosophy-of-art posthumanism-and-responsibility</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4898b104bab0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:criticism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aesthetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:posthumanism-and-responsibility"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1806.04510">
    <title>[1806.04510] Dank Learning: Generating Memes Using Deep Neural Networks</title>
    <dc:date>2018-06-14T14:20:40+00:00</dc:date>
    <link>https://arxiv.org/abs/1806.04510</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We introduce a novel meme generation system, which given any image can produce a humorous and relevant caption. Furthermore, the system can be conditioned on not only an image but also a user-defined label relating to the meme template, giving a handle to the user on meme content. The system uses a pretrained Inception-v3 network to return an image embedding which is passed to an attention-based deep-layer LSTM model producing the caption - inspired by the widely recognised Show and Tell Model. We implement a modified beam search to encourage diversity in the captions. We evaluate the quality of our model using perplexity and human assessment on both the quality of memes generated and whether they can be differentiated from real ones. Our model produces original memes that cannot on the whole be differentiated from real ones.
]]></description>
<dc:subject>neural-networks generative-art generative-models rather-interesting amusing to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:90c6b1a71e91/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:amusing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.surfacemag.com/articles/vera-molnar-in-thinking-machines-at-moma/">
    <title>The Artist Who Drew With Computers, Before Computers Were a Thing - SURFACE</title>
    <dc:date>2018-05-07T11:46:01+00:00</dc:date>
    <link>https://www.surfacemag.com/articles/vera-molnar-in-thinking-machines-at-moma/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[“What makes Molnár’s work so important today is that her ability to experiment was aided and amplified by the tools she used,” say Anderson and Bianconi. “This spirit of experimentation allowed these works to be both systematic and humanistic, and has been influential for artists who have worked with computers since.”
]]></description>
<dc:subject>generative-art art-criticism history to-write-about exhibition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5aa61e0b4486/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:art-criticism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:exhibition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1804.01622">
    <title>[1804.01622] Image Generation from Scene Graphs</title>
    <dc:date>2018-04-15T10:05:44+00:00</dc:date>
    <link>https://arxiv.org/abs/1804.01622</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[To truly understand the visual world our models should be able not only to recognize images but also generate them. To this end, there has been exciting recent progress on generating images from natural language descriptions. These methods give stunning results on limited domains such as descriptions of birds or flowers, but struggle to faithfully reproduce complex sentences with many objects and relationships. To overcome this limitation we propose a method for generating images from scene graphs, enabling explicitly reasoning about objects and their relationships. Our model uses graph convolution to process input graphs, computes a scene layout by predicting bounding boxes and segmentation masks for objects, and converts the layout to an image with a cascaded refinement network. The network is trained adversarially against a pair of discriminators to ensure realistic outputs. We validate our approach on Visual Genome and COCO-Stuff, where qualitative results, ablations, and user studies demonstrate our method's ability to generate complex images with multiple objects.
]]></description>
<dc:subject>deep-learning image-processing generative-art generative-models turning-the-handle-the-other-way to-write-about to-do</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3f79cf6bdc72/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:turning-the-handle-the-other-way"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-do"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://laudatortemporisacti.blogspot.com/2018/04/the-poligs-of-oern-vent.html">
    <title>Laudator Temporis Acti: The Poligs of the Oern Vent</title>
    <dc:date>2018-04-09T12:30:33+00:00</dc:date>
    <link>http://laudatortemporisacti.blogspot.com/2018/04/the-poligs-of-oern-vent.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA['The Poligs of the Oern Vent in dugard to the Brounincinl Coutrick is the colic of the unscrifulouse Gawler.' So ran the printed slip technically known as a 'rough proof'. The Aryan had surpassed himself; but, as he read, light filled the mind of the Reader. He had written — 'The policy of the Government in regard to the Provincial Contract is the policy of the unscrupulous lawyer', and, behold, with a mere turn of his wrist, the Aryan had glorified, and enriched with the wealth of an exuberant Orientalism that simple sentence, till it stood forth a gem, or rather a collection of gems! 'The Poligs of the Oern Vent' — George Meredith might have woven those words into the Shaving of Shagpat, and so made that dazzling piece of broidery yet more gorgeous. 'Brounincinl Coutrick' would suit admirably the manager of a travelling-circus. Conceive the effect, on white and red posters of: — 'To-night! To-night!! To-night!!! The Brounincinl Coutrick!' The words would draw thousands — millions. 'Unscrifulouse Gawler' again would furnish an absolutely unique and startling title for a semi-humourous, semi-grotesque, wholly-horrible story, of the American school, let us say. Think for a moment what fashion of ghoulo-demoniacal, triple-Quilpian, Jekyll-and-Hydeous character, the 'unscrifulouse Gawler' would be. Out of the incult wantonings of a Punjabi Mahommedan with a box of type, had been born the suggestions of three Brilliant Notions, did any man care to use them, exactly as ideas for patterns are conveyed to the designer by the chance-ruled twists of the Kaleidescope.
]]></description>
<dc:subject>nanohistory generative-art inspiration unexpected-similarities Lewis-Carroll seek-ye-the-original</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ba4ef5c74385/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nanohistory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inspiration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:unexpected-similarities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Lewis-Carroll"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:seek-ye-the-original"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://softologyblog.wordpress.com/2018/02/05/history-dependant-cellular-automata/">
    <title>History Dependent Cellular Automata | Softology's Blog</title>
    <dc:date>2018-04-06T13:55:24+00:00</dc:date>
    <link>https://softologyblog.wordpress.com/2018/02/05/history-dependant-cellular-automata/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[I have been exploring a variety of cellular automata lately and here is another one.

This is from another idea I had. Andrew Adamatzky let me know there has been work done using previous states before referred to as “Cellular Automata with Memory”. See these papers by Ramon Alonso-Sanz for other examples of 1D and 2D CAs using memory from previous generations.

This is a totalistic CA that uses the usual 8 immediate neighbor cells as well as the last step’s current cell and 8 neighbors. This gives a total of 17 neighbor cells that can influence the birth and survival of the cells.

I call them “History Dependent Cellular Automata” because they depend on the previous cycles’ neighbor cells as well as the usual 8 immediate neighbor cells.]]></description>
<dc:subject>cellular-automata generative-art to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1a3cb48426f5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.contextfreeart.org/index.html">
    <title>Context Free Art</title>
    <dc:date>2018-04-06T13:53:48+00:00</dc:date>
    <link>https://www.contextfreeart.org/index.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Context Free is a program that generates images from written instructions called a grammar. The program follows the instructions in a few seconds to create images that can contain millions of shapes.

]]></description>
<dc:subject>generative-art rather-interesting graphics programming-language to-understand</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:03dd966ab1aa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:graphics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:programming-language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://becominghuman.ai/creating-intricate-art-with-neural-style-transfer-e5fee5f89481">
    <title>Creating Intricate Art with Neural Style Transfer – Becoming Human: Artificial Intelligence Magazine</title>
    <dc:date>2018-02-03T18:15:51+00:00</dc:date>
    <link>https://becominghuman.ai/creating-intricate-art-with-neural-style-transfer-e5fee5f89481</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The architecture is based on Gatys’ style transfer algorithm with a few minor modifications. In this case, the content image is a silhouette and style image can be any pattern (ranging from simple black and white doodle to more complex color mosaics). The code also contains a module to invert and create a mask based on the content image, which will eventually be applied to the generated pattern.
]]></description>
<dc:subject>generative-art generative-models style-transfer computational-recreations to-write-about to-do nudge-targets consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:25607977fabf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:style-transfer"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-do"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1712.00714">
    <title>[1712.00714] Spatial PixelCNN: Generating Images from Patches</title>
    <dc:date>2017-12-26T12:59:38+00:00</dc:date>
    <link>https://arxiv.org/abs/1712.00714</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper we propose Spatial PixelCNN, a conditional autoregressive model that generates images from small patches. By conditioning on a grid of pixel coordinates and global features extracted from a Variational Autoencoder (VAE), we are able to train on patches of images, and reproduce the full-sized image. We show that it not only allows for generating high quality samples at the same resolution as the underlying dataset, but is also capable of upscaling images to arbitrary resolutions (tested at resolutions up to 50×) on the MNIST dataset. Compared to a PixelCNN++ baseline, Spatial PixelCNN quantitatively and qualitatively achieves similar performance on the MNIST dataset.]]></description>
<dc:subject>deep-learning neural-networks generative-art generative-models to-write-about to-do nudge-targets consider:symbolic-regression</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:27c09e1fdf11/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-do"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:symbolic-regression"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.joelsimon.net/ecosystem-modelling.html">
    <title>Joel Simon</title>
    <dc:date>2017-12-23T10:14:53+00:00</dc:date>
    <link>http://www.joelsimon.net/ecosystem-modelling.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[An abstract plant multi-agent ecosystem simulation for artistic and research purposes. Individuals are simple circles that grow, collect resources, spread seeds and compete with others each timestep. Models are used in ecology by using the output, and its discrepancy from observation, to generate new hypotheses about ecological mechanics. I was inspired to do this project while working on an upcoming project modeling coral growth. I don't have a background in ecosystem modeling but the ideas came from conversations with a PhD student who does. I did an interview on my design and research interest in this project and its relationship to my virtual coral project. The code is available on Github.

]]></description>
<dc:subject>generative-art computational-art complexology agent-based</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bec9654b4931/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://distill.pub/2017/feature-visualization/">
    <title>Feature Visualization</title>
    <dc:date>2017-11-09T23:20:14+00:00</dc:date>
    <link>https://distill.pub/2017/feature-visualization/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This article focusses on feature visualization. While feature visualization is a powerful tool, actually getting it to work involves a number of details. In this article, we examine the major issues and explore common approaches to solving them. We ﬁnd that remarkably simple methods can produce high-quality visualizations. Along the way we introduce a few tricks for exploring variation in what neurons react to, how they interact, and how to improve the optimization process.

]]></description>
<dc:subject>neural-networks inverse-problems generative-art to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fca13690b659/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inverse-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1705.04058">
    <title>[1705.04058] Neural Style Transfer: A Review</title>
    <dc:date>2017-10-20T17:05:13+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.04058</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The recent work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNN) in creating artistic fantastic imagery by separating and recombing the image content and style. This process of using CNN to migrate the semantic content of one image to different styles is referred to as Neural Style Transfer. Since then, Neural Style Transfer has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention from computer vision researchers and several methods are proposed to either improve or extend the original neural algorithm proposed by Gatys et al. However, there is no comprehensive survey presenting and summarizing recent Neural Style Transfer literature. This review aims to provide an overview of the current progress towards Neural Style Transfer, as well as discussing its various applications and open problems for future research.]]></description>
<dc:subject>generative-art neural-networks convolutional-networks algorithms rather-interesting nudge-targets machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ee3cfad87c60/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:convolutional-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1704.08292">
    <title>[1704.08292] Deep Cross-Modal Audio-Visual Generation</title>
    <dc:date>2017-10-11T00:39:22+00:00</dc:date>
    <link>https://arxiv.org/abs/1704.08292</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Cross-modal audio-visual perception has been a long-lasting topic in psychology and neurology, and various studies have discovered strong correlations in human perception of auditory and visual stimuli. Despite works in computational multimodal modeling, the problem of cross-modal audio-visual generation has not been systematically studied in the literature. In this paper, we make the first attempt to solve this cross-modal generation problem leveraging the power of deep generative adversarial training. Specifically, we use conditional generative adversarial networks to achieve cross-modal audio-visual generation of musical performances. We explore different encoding methods for audio and visual signals, and work on two scenarios: instrument-oriented generation and pose-oriented generation. Being the first to explore this new problem, we compose two new datasets with pairs of images and sounds of musical performances of different instruments. Our experiments using both classification and human evaluations demonstrate that our model has the ability to generate one modality, i.e., audio/visual, from the other modality, i.e., visual/audio, to a good extent. Our experiments on various design choices along with the datasets will facilitate future research in this new problem space.
]]></description>
<dc:subject>deep-learning neural-networks generative-art generative-models rather-interesting video computer-vision nudge-targets consider:rediscovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7f7b50824ff2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:video"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computer-vision"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.andrewdavidson.com/gibberish/?companyname=Floopr">
    <title>Corporate Gibberish Generator on AndrewDavidson.com</title>
    <dc:date>2017-10-05T10:44:28+00:00</dc:date>
    <link>http://www.andrewdavidson.com/gibberish/?companyname=Floopr</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Welcome to the Corporate Gibberish Generator™ by Andrew Davidson. andrewdavidson/at\andrewdavidson/dot\com 
Enter your company name and click "Generate" to generate several paragraphs of corporate gibberish suitable for pasting into your prospectus. 
(The gibberish is geared more toward Internet and technology companies.)]]></description>
<dc:subject>branding corporatism humor algorithms natural-language-processing generative-art</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ea3bac96cd08/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:branding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:corporatism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:humor"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:natural-language-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://inconvergent.net/spline-script/">
    <title>Spline Script · inconvergent</title>
    <dc:date>2017-10-05T10:42:39+00:00</dc:date>
    <link>http://inconvergent.net/spline-script/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[It's been a while since the last time I was down the rabbit hole of generative handwriting. However, I thought I'd describe the experiments I've been doing over the past week or so in some more detail. 

]]></description>
<dc:subject>generative-art rather-interesting to-do to-write-about algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:70139c9fa75d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-do"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.cmu.edu/~aayushb/pixelNN/">
    <title>PixelNN</title>
    <dc:date>2017-09-25T11:34:46+00:00</dc:date>
    <link>http://www.cs.cmu.edu/~aayushb/pixelNN/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an "incomplete" signal such as a low-resolution image, a surface normal map, or edges. Current state-of-the-art deep generative models designed for such conditional image synthesis lack two important things: (1) they are unable to generate a large set of diverse outputs, due to the mode collapse problem. (2) they are not interpretable, making it difficult to control the synthesized output. We demonstrate that NN approaches potentially address such limitations, but suffer in accuracy on small datasets. We design a simple pipeline that combines the best of both worlds: the first stage uses a convolutional neural network (CNN) to maps the input to a (overly-smoothed) image, and the second stage uses a pixel-wise nearest neighbor method to map the smoothed output to multiple high-quality, high-frequency outputs in a controllable manner. We demonstrate our approach for various input modalities, and for various domains ranging from human faces to cats-and-dogs to shoes and handbags.

]]></description>
<dc:subject>neural-networks generative-art generative-models rather-interesting to-do to-write-about consider:architecture nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1dcbe2fb7b32/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-do"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:architecture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/pkmital/pycadl">
    <title>pkmital/pycadl: Python package with source code from the course &quot;Creative Applications of Deep Learning w/ TensorFlow&quot;</title>
    <dc:date>2017-09-14T11:55:11+00:00</dc:date>
    <link>https://github.com/pkmital/pycadl</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This package is part of the Kadenze Academy program Creative Applications of Deep Learning w/ TensorFlow.

]]></description>
<dc:subject>python deep-learning class library generative-art generative-models neural-networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:be9cac05fef7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:class"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:library"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://simblob.blogspot.com/2017/09/mapgen2-html5.html">
    <title>Blobs in Games: Polygonal Map Generation, HTML5 version</title>
    <dc:date>2017-09-14T11:31:01+00:00</dc:date>
    <link>http://simblob.blogspot.com/2017/09/mapgen2-html5.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Seven years ago I worked on an terrain generator for a game called Realm of the Mad God. We had started out using Perlin Noise for height maps but found that most of the maps we generated weren't a good fit for the game. I spent the summer trying out ideas for making the maps, and discovered that Voronoi Diagrams could form a good “skeleton” for making maps. The combination of Voronoi polygons and Delaunay triangles gave me places for quests, towns, rivers, and roads. I used Perlin Noise for the coastlines instead of for the height map. The resulting maps were unrealistic and inflexible but they were just what we needed for our game.

]]></description>
<dc:subject>generative-models generative-art algorithms rather-interesting games learning-in-public to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8be85b103b5e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-in-public"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.redblobgames.com/maps/mapgen2/">
    <title>Polygon map generator</title>
    <dc:date>2017-09-14T11:29:53+00:00</dc:date>
    <link>http://www.redblobgames.com/maps/mapgen2/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Feel free to use the map generator in your projects! For other projects, I would use some of the same core algorithms but might assign coastlines, mountains, and biomes differently than what I did for this project. This project needed coastlines to be interesting island shapes. Another project might need maps not completely surrounded by water. This project needed mountains to be in the center of the island. Another project might needs continents, where mountains are not limited to being in the center. Or it may need parallel mountain ranges. This project needed smooth elevation. Another project might need caves, cliffs, canyons, or chasms. This project needed simple biomes based on distance to coastline and distance to water. Another project might need biomes based on latitude and rainfall, which could be influenced by wind, which could be influenced by mountain ranges or weather systems. There are a lot of variants to explore!

]]></description>
<dc:subject>algorithms tutorial rather-interesting generative-art generative-models to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0464365981a8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1708.09321">
    <title>[1708.09321] Adversarial nets with perceptual losses for text-to-image synthesis</title>
    <dc:date>2017-09-02T13:27:22+00:00</dc:date>
    <link>https://arxiv.org/abs/1708.09321</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text. Despite the overall fair quality, the generated images often expose visible flaws that lack structural definition for an object of interest. In this paper, we aim to extend state of the art for GAN-based text-to-image synthesis by improving perceptual quality of generated images. Differentiated from previous work, our synthetic image generator optimizes on perceptual loss functions that measure pixel, feature activation, and texture differences against a natural image. We present visually more compelling synthetic images of birds and flowers generated from text descriptions in comparison to some of the most prominent existing work.
]]></description>
<dc:subject>generative-art generative-models neural-networks deep-learning image-processing image-synthesis to-write-about to-do</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:efb85bb6c48b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-synthesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-do"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1705.04098">
    <title>[1705.04098] A Generative Model of People in Clothing</title>
    <dc:date>2017-08-12T13:18:49+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.04098</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present the first image-based generative model of people in clothing for the full body. We sidestep the commonly used complex graphics rendering pipeline and the need for high-quality 3D scans of dressed people. Instead, we learn generative models from a large image database. The main challenge is to cope with the high variance in human pose, shape and appearance. For this reason, pure image-based approaches have not been considered so far. We show that this challenge can be overcome by splitting the generating process in two parts. First, we learn to generate a semantic segmentation of the body and clothing. Second, we learn a conditional model on the resulting segments that creates realistic images. The full model is differentiable and can be conditioned on pose, shape or color. The result are samples of people in different clothing items and styles. The proposed model can generate entirely new people with realistic clothing. In several experiments we present encouraging results that suggest an entirely data-driven approach to people generation is possible.
]]></description>
<dc:subject>generative-art generative-models image-processing machine-learning rather-interesting to-bot to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7690b3403443/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-bot"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1703.02826">
    <title>[1703.02826] A Linear Extrinsic Calibration of Kaleidoscopic Imaging System from Single 3D Point</title>
    <dc:date>2017-07-22T13:01:10+00:00</dc:date>
    <link>https://arxiv.org/abs/1703.02826</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper proposes a new extrinsic calibration of kaleidoscopic imaging system by estimating normals and distances of the mirrors. The problem to be solved in this paper is a simultaneous estimation of all mirror parameters consistent throughout multiple reflections. Unlike conventional methods utilizing a pair of direct and mirrored images of a reference 3D object to estimate the parameters on a per-mirror basis, our method renders the simultaneous estimation problem into solving a linear set of equations. The key contribution of this paper is to introduce a linear estimation of multiple mirror parameters from kaleidoscopic 2D projections of a single 3D point of unknown geometry. Evaluations with synthesized and real images demonstrate the performance of the proposed algorithm in comparison with conventional methods.
]]></description>
<dc:subject>generative-art linear-algebra image-processing rather-interesting algorithms performance-measure to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c93379ab3eaa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:linear-algebra"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://machinelearning.apple.com/2017/07/07/GAN.html">
    <title>Improving the Realism of Synthetic Images - Apple</title>
    <dc:date>2017-07-21T12:54:36+00:00</dc:date>
    <link>https://machinelearning.apple.com/2017/07/07/GAN.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Most successful examples of neural nets today are trained with supervision. However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly. An alternative to labelling huge amounts of data is to use synthetic images from a simulator. This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images. To help close this performance gap, we’ve developed a method for refining synthetic images to make them look more realistic. We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.
]]></description>
<dc:subject>apple machine-learning image-processing generative-art rather-interesting to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d9c92e8e820c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:apple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.autogamedesign.eu/surprise-search">
    <title>Surprise Search | Autonomous Computational Game Designers</title>
    <dc:date>2017-06-03T11:49:52+00:00</dc:date>
    <link>http://www.autogamedesign.eu/surprise-search</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Surprise search is a novel algorithm that takes inspiration from the notion of surprise for unconventional discovery in computational creativity.

The algorithm mimics the self-surprise cognitive process of creativity and equips computational creators with the ability to search for outcomes that deviate from the algorithm’s expected behavior or process.

]]></description>
<dc:subject>generative-art generative-models novelty-search machine-learning algorithms to-write-about to-do</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b701695f616b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:novelty-search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-do"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/0705.1142v1">
    <title>[0705.1142v1] A primer on substitution tilings of the Euclidean plane</title>
    <dc:date>2017-04-29T11:04:08+00:00</dc:date>
    <link>https://arxiv.org/abs/0705.1142v1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper is intended to provide an introduction to the theory of substitution tilings. For our purposes, tiling substitution rules are divided into two broad classes: geometric and combinatorial. Geometric substitution tilings include self-similar tilings such as the well-known Penrose tilings; for this class there is a substantial body of research in the literature. Combinatorial substitutions are just beginning to be examined, and some of what we present here is new. We give numerous examples, mention selected major results, discuss connections between the two classes of substitutions, include current research perspectives and questions, and provide an extensive bibliography. Although the author attempts to fairly represent the as a whole, the paper is not an exhaustive survey, and she apologizes for any important omissions.
]]></description>
<dc:subject>tiling self-similarity combinatorics rewriting-systems generative-art rather-interesting to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:23ed058fe10f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tiling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-similarity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:combinatorics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rewriting-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1403.6566">
    <title>[1403.6566] Image Retargeting by Content-Aware Synthesis</title>
    <dc:date>2017-04-23T10:51:49+00:00</dc:date>
    <link>https://arxiv.org/abs/1403.6566</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Real-world images usually contain vivid contents and rich textural details, which will complicate the manipulation on them. In this paper, we design a new framework based on content-aware synthesis to enhance content-aware image retargeting. By detecting the textural regions in an image, the textural image content can be synthesized rather than simply distorted or cropped. This method enables the manipulation of textural & non-textural regions with different strategy since they have different natures. We propose to retarget the textural regions by content-aware synthesis and non-textural regions by fast multi-operators. To achieve practical retargeting applications for general images, we develop an automatic and fast texture detection method that can detect multiple disjoint textural regions. We adjust the saliency of the image according to the features of the textural regions. To validate the proposed method, comparisons with state-of-the-art image targeting techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.
]]></description>
<dc:subject>generative-art generative-models image-processing machine-learning nudge-targets consider:performance-measures consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:88dd63c2f80c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:performance-measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1704.00112">
    <title>[1704.00112] Configurable, Photorealistic Image Rendering and Ground Truth Synthesis by Sampling Stochastic Grammars Representing Indoor Scenes</title>
    <dc:date>2017-04-10T09:56:04+00:00</dc:date>
    <link>https://arxiv.org/abs/1704.00112</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We propose the configurable rendering of massive quantities of photorealistic images with ground truth for the purposes of training, benchmarking, and diagnosing computer vision models. In contrast to the conventional (crowd-sourced) manual labeling of ground truth for a relatively modest number of RGB-D images captured by Kinect-like sensors, we devise a non-trivial configurable pipeline of algorithms capable of generating a potentially infinite variety of indoor scenes using a stochastic grammar, specifically, one represented by an attributed spatial And-Or graph. We employ physics-based rendering to synthesize photorealistic RGB images while automatically synthesizing detailed, per-pixel ground truth data, including visible surface depth and normal, object identity and material information, as well as illumination. Our pipeline is configurable inasmuch as it enables the precise customization and control of important attributes of the generated scenes. We demonstrate that our generated scenes achieve a performance similar to the NYU v2 Dataset on pre-trained deep learning models. By modifying pipeline components in a controllable manner, we furthermore provide diagnostics on common scene understanding tasks; eg., depth and surface normal prediction, semantic segmentation, etc.
]]></description>
<dc:subject>image-processing neural-networks generative-models generative-art data-fusion rather-interesting nudge-targets consider:similar-grammars</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:31affdc43639/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-fusion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:similar-grammars"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://shonkwiler.org/art">
    <title>Clayton Shonkwiler — Art</title>
    <dc:date>2017-03-20T11:29:48+00:00</dc:date>
    <link>http://shonkwiler.org/art</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>artist via:prior-link geometry generative-art rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5050e2e69d14/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artist"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:prior-link"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://creators.vice.com/en_us/article/what-happens-when-humans-use-animals-to-make-art">
    <title>This Is What Happens When Humans Use Animals to Make Art - Creators</title>
    <dc:date>2017-02-28T11:48:58+00:00</dc:date>
    <link>https://creators.vice.com/en_us/article/what-happens-when-humans-use-animals-to-make-art</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Each work in the exhibition is the result of an artistic "collaboration" between a human and an animal, ranging from honeybees and spiders to a sapsucker bird. Different than art that depicts or uses animals in some way—Damien Hirst's shark in formaldehyde or Jeff Koons' countless Balloon Dog sculptures—the works in Animal Intent literally incorporate interventions and creations made by animals as part of each work's material or process. 

]]></description>
<dc:subject>art via:twitter generative-art collaboration to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:95ecef6645fd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:twitter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collaboration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.youtube.com/watch?v=50-d_J0hKz0">
    <title>Manuel Delanda, &quot;Deleuze and the Use of the Genetic Algorithm in Architecture&quot; - YouTube</title>
    <dc:date>2017-02-12T14:32:11+00:00</dc:date>
    <link>https://www.youtube.com/watch?v=50-d_J0hKz0</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>philosophy generative-art philosophy-of-engineering lecture video</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:61b176341454/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:lecture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:video"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/schenker/image-learner/">
    <title>schenker/image-learner: Training a neural network to map from x,y of an images pixels to r,g,b.</title>
    <dc:date>2017-02-07T11:52:32+00:00</dc:date>
    <link>https://github.com/schenker/image-learner/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Train a neural network to map from x and y coordinate of each pixel to the pixels r,g,b values. The network has 2 inputs (x and y), several fully connected (highway) layers and three outputs (r, g and b).

]]></description>
<dc:subject>generative-art rather-interesting nudge-targets consider:looking-to-see to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:626fa298cd0c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://appsrv.cse.cuhk.edu.hk/~ttwong/papers/pad/pad.html">
    <title>Pyramid of Arclength Descriptor for Generating Collage of Shapes</title>
    <dc:date>2017-01-24T12:17:49+00:00</dc:date>
    <link>https://appsrv.cse.cuhk.edu.hk/~ttwong/papers/pad/pad.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper tackles a challenging 2D collage generation problem, focusing on shapes: we aim to fill a given region by packing irregular and reasonably-sized shapes with minimized gaps and overlaps. To achieve this nontrivial problem, we first have to analyze the boundary of individual shapes and then couple the shapes with partially-matched boundary to reduce gaps and overlaps in the collages. Second, the search space in identifying a good coupling of shapes is highly enormous, since arranging a shape in a collage involves a position, an orientation, and a scale factor. Yet, this matching step needs to be performed for every single shape when we pack it into a collage. Existing shape descriptors are simply infeasible for computation in a reasonable amount of time. To overcome this, we present a brand new, scale- and rotation-invariant 2D shape descriptor, namely pyramid of arclength descriptor (PAD). Its formulation is locally supported, scalable, and yet simple to construct and compute. These properties make PAD efficient for performing the partial-shape matching. Hence, we can prune away most search space with simple calculation, and efficiently identify candidate shapes. We evaluate our method using a large variety of shapes with different types and contours. Convincing collage results in terms of visual quality and time performance are obtained. 

]]></description>
<dc:subject>graphics optimization algorithms rather-interesting generative-art performance-measure</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0054ffaf3085/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:graphics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>