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    <description>recent bookmarks from arsyed</description>
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  </channel><item rdf:about="https://arxiv.org/abs/2110.13985">
    <title>[2110.13985] Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers</title>
    <dc:date>2021-11-04T16:47:06+00:00</dc:date>
    <link>https://arxiv.org/abs/2110.13985</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling power and computational efficiency. We introduce a simple sequence model inspired by control systems that generalizes these approaches while addressing their shortcomings. The Linear State-Space Layer (LSSL) maps a sequence u↦y by simply simulating a linear continuous-time state-space representation x˙=Ax+Bu,y=Cx+Du. Theoretically, we show that LSSL models are closely related to the three aforementioned families of models and inherit their strengths. For example, they generalize convolutions to continuous-time, explain common RNN heuristics, and share features of NDEs such as time-scale adaptation. We then incorporate and generalize recent theory on continuous-time memorization to introduce a trainable subset of structured matrices A that endow LSSLs with long-range memory. Empirically, stacking LSSL layers into a simple deep neural network obtains state-of-the-art results across time series benchmarks for long dependencies in sequential image classification, real-world healthcare regression tasks, and speech. On a difficult speech classification task with length-16000 sequences, LSSL outperforms prior approaches by 24 accuracy points, and even outperforms baselines that use hand-crafted features on 100x shorter sequences."]]></description>
<dc:subject>sequence-modeling rnn cnn neural-net</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:a18a57b22a46/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2111.00396#">
    <title>[2111.00396] Efficiently Modeling Long Sequences with Structured State Spaces</title>
    <dc:date>2021-11-04T16:44:06+00:00</dc:date>
    <link>https://arxiv.org/abs/2111.00396#</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range dependencies. Although conventional models including RNNs, CNNs, and Transformers have specialized variants for capturing long dependencies, they still struggle to scale to very long sequences of 10000 or more steps. A promising recent approach proposed modeling sequences by simulating the fundamental state space model (SSM) \( x'(t) = Ax(t) + Bu(t), y(t) = Cx(t) + Du(t) \), and showed that for appropriate choices of the state matrix \( A \), this system could handle long-range dependencies mathematically and empirically. However, this method has prohibitive computation and memory requirements, rendering it infeasible as a general sequence modeling solution. We propose the Structured State Space (S4) sequence model based on a new parameterization for the SSM, and show that it can be computed much more efficiently than prior approaches while preserving their theoretical strengths. Our technique involves conditioning \( A \) with a low-rank correction, allowing it to be diagonalized stably and reducing the SSM to the well-studied computation of a Cauchy kernel. S4 achieves strong empirical results across a diverse range of established benchmarks, including (i) 91\% accuracy on sequential CIFAR-10 with no data augmentation or auxiliary losses, on par with a larger 2-D ResNet, (ii) substantially closing the gap to Transformers on image and language modeling tasks, while performing generation 60× faster (iii) SoTA on every task from the Long Range Arena benchmark, including solving the challenging Path-X task of length 16k that all prior work fails on, while being as efficient as all competitors. "]]></description>
<dc:subject>state-space sequence-modeling rnn cnn neural-net</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:2bff91b839da/</dc:identifier>
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</item>
<item rdf:about="https://arxiv.org/abs/2109.12975">
    <title>[2109.12975] Towards a Theory of Bullshit Visualization</title>
    <dc:date>2021-10-29T14:59:25+00:00</dc:date>
    <link>https://arxiv.org/abs/2109.12975</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["In this unhinged rant, I lay out my suspicion that a lot of visualizations are bullshit: charts that do not have even the common decency to intentionally lie but are totally unconcerned about the state of the world or any practical utility. I suspect that bullshit charts take up a large fraction of the time and attention of actual visualization producers and consumers, and yet are seemingly absent from academic research into visualization design."]]></description>
<dc:subject>visualization bullshit</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:0047f9fb531c/</dc:identifier>
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<item rdf:about="https://hyperfuture.cs.columbia.edu/">
    <title>Learning the Predictability of the Future - Columbia Computer Vision</title>
    <dc:date>2021-10-18T15:58:43+00:00</dc:date>
    <link>https://hyperfuture.cs.columbia.edu/</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["We introduce a framework for learning from unlabeled video what is predictable in the future. Instead of committing up front to features to predict, our approach learns from data which features are predictable.

Based on the observation that hyperbolic geometry naturally and compactly encodes hierarchical structure, we propose a predictive model in hyperbolic space. When the model is most confident, it will predict at a concrete level of the hierarchy, but when the model is not confident, it learns to automatically select a higher level of abstraction. Experiments on two established datasets show the key role of hierarchical representations for action prediction. Although our representation is trained with unlabeled video, visualizations show that action hierarchies emerge in the representation."]]></description>
<dc:subject>self-supervision hyperbolic-geometry poincare-embedding uncertainty hierarchy prediction</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:8b59535251b8/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:poincare-embedding"/>
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<item rdf:about="http://blog.felipe.rs/2017/07/07/where-do-type-systems-come-from/">
    <title>Where do Type Systems Come From?</title>
    <dc:date>2021-10-11T20:22:35+00:00</dc:date>
    <link>http://blog.felipe.rs/2017/07/07/where-do-type-systems-come-from/</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["In short, type theory was developed to be an alternative to set theory as the foundation of mathematical proofs in symbolic logic due to its ability to solve some contradictions stemming from naive set theory."]]></description>
<dc:subject>types math</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:7ab5292b4e0e/</dc:identifier>
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<item rdf:about="https://github.com/wimpysworld/quickemu">
    <title>wimpysworld/quickemu: Quickly create and run optimised Windows, macOS and Linux desktop virtual machines.</title>
    <dc:date>2021-10-11T20:20:51+00:00</dc:date>
    <link>https://github.com/wimpysworld/quickemu</link>
    <dc:creator>arsyed</dc:creator><dc:subject>software vm qemu</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:a934f5971756/</dc:identifier>
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<item rdf:about="https://www.nature.com/articles/d41586-021-02483-w">
    <title>The tangled history of mRNA vaccines</title>
    <dc:date>2021-10-07T16:20:06+00:00</dc:date>
    <link>https://www.nature.com/articles/d41586-021-02483-w</link>
    <dc:creator>arsyed</dc:creator><dc:subject>mrna vaccine history</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:3e3d8d730d1a/</dc:identifier>
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<item rdf:about="https://belkadan.com/blog/2020/04/Shallow-Git-Repositories/">
    <title>Shallow Git Repositories // -dealloc</title>
    <dc:date>2021-09-22T20:59:34+00:00</dc:date>
    <link>https://belkadan.com/blog/2020/04/Shallow-Git-Repositories/</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["When I was getting the code in the previous post ready to share, I ran into a problem: my checkouts of LLVM and Swift were shallow clones, i.e. git repositories that don’t store the full history of each branch. Working with those locally is surprisingly easy; trying to set them up on a server using git push is a bit trickier. While trying to figure out what was going on, I was dismayed by the lack of up-to-date documentation about shallow repositories, even on my usual go-to site, git-scm.com. So here’s a collection of information I’ve gathered about shallow repositories."]]></description>
<dc:subject>git shallow</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:e20dee29872a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:git"/>
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<item rdf:about="https://press.princeton.edu/books/paperback/9780691203706/visual-differential-geometry-and-forms">
    <title>Visual Differential Geometry and Forms | Princeton University Press</title>
    <dc:date>2021-08-24T04:52:27+00:00</dc:date>
    <link>https://press.princeton.edu/books/paperback/9780691203706/visual-differential-geometry-and-forms</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Visual Differential Geometry and Forms fulfills two principal goals. In the first four acts, Tristan Needham puts the geometry back into differential geometry. Using 235 hand-drawn diagrams, Needham deploys Newton’s geometrical methods to provide geometrical explanations of the classical results. In the fifth act, he offers the first undergraduate introduction to differential forms that treats advanced topics in an intuitive and geometrical manner.

Unique features of the first four acts include: four distinct geometrical proofs of the fundamentally important Global Gauss-Bonnet theorem, providing a stunning link between local geometry and global topology; a simple, geometrical proof of Gauss’s famous Theorema Egregium; a complete geometrical treatment of the Riemann curvature tensor of an n-manifold; and a detailed geometrical treatment of Einstein’s field equation, describing gravity as curved spacetime (General Relativity), together with its implications for gravitational waves, black holes, and cosmology. The final act elucidates such topics as the unification of all the integral theorems of vector calculus; the elegant reformulation of Maxwell’s equations of electromagnetism in terms of 2-forms; de Rham cohomology; differential geometry via Cartan’s method of moving frames; and the calculation of the Riemann tensor using curvature 2-forms. Six of the seven chapters of Act V can be read completely independently from the rest of the book.

Requiring only basic calculus and geometry, Visual Differential Geometry and Forms provocatively rethinks the way this important area of mathematics should be considered and taught."]]></description>
<dc:subject>books math differential-geometry</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:7f1c866364aa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:books"/>
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</item>
<item rdf:about="https://intel.github.io/scikit-learn-intelex/">
    <title>Intel(R) Extension for Scikit-learn* — Intel(R) Extension for Scikit-learn* 2021.2.1 documentation</title>
    <dc:date>2021-06-22T18:48:47+00:00</dc:date>
    <link>https://intel.github.io/scikit-learn-intelex/</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application. The acceleration is achieved through the use of the Intel(R) oneAPI Data Analytics Library (oneDAL). Patching scikit-learn makes it a well-suited machine learning framework for dealing with real-life problems."]]></description>
<dc:subject>python libs sklearn intel performance</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:389c004c8e96/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:intel"/>
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<item rdf:about="https://bc8a0d7f-8c74-47ae-b6cb-628620d615df.filesusr.com/ugd/b740ad_8793a5535f304e43835247045c2c27d3.pdf">
    <title>Myths of Meritocracy, Friendship, and Fun Work: Class andGender in North American Academic Communities</title>
    <dc:date>2021-05-27T16:35:12+00:00</dc:date>
    <link>https://bc8a0d7f-8c74-47ae-b6cb-628620d615df.filesusr.com/ugd/b740ad_8793a5535f304e43835247045c2c27d3.pdf</link>
    <dc:creator>arsyed</dc:creator><dc:subject>culture academia meritocracy gender class</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:5e15224b6845/</dc:identifier>
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<item rdf:about="https://github.com/AlexandreDecan/portion">
    <title>GitHub - AlexandreDecan/portion: portion, a Python library providing data structure and operations for intervals.</title>
    <dc:date>2021-05-14T19:53:25+00:00</dc:date>
    <link>https://github.com/AlexandreDecan/portion</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["The portion library (formerly distributed as python-intervals) provides data structure and operations for intervals in Python 3.6+.

    Support intervals of any (comparable) objects.
    Closed or open, finite or (semi-)infinite intervals.
    Interval sets (union of atomic intervals) are supported.
    Automatic simplification of intervals.
    Support comparison, transformation, intersection, union, complement, difference and containment.
    Provide test for emptiness, atomicity, overlap and adjacency.
    Discrete iterations on the values of an interval.
    Dict-like structure to map intervals to data.
    Import and export intervals to strings and to Python built-in data types.
    Heavily tested with high code coverage."]]></description>
<dc:subject>python libs interval</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:70cc538ea7b3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:interval"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://openreview.net/forum?id=lmTWnm3coJJ">
    <title>Robust Curriculum Learning: from clean label detection to noisy label self-correction | OpenReview</title>
    <dc:date>2021-05-07T22:14:56+00:00</dc:date>
    <link>https://openreview.net/forum?id=lmTWnm3coJJ</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Neural network training can easily overfit noisy labels resulting in poor generalization performance. Existing methods address this problem by (1) filtering out the noisy data and only using the clean data for training or (2) relabeling the noisy data by the model during training or by another model trained only on a clean dataset. However, the former does not leverage the features' information of wrongly-labeled data, while the latter may produce wrong pseudo-labels for some data and introduce extra noises. In this paper, we propose a smooth transition and interplay between these two strategies as a curriculum that selects training samples dynamically. In particular, we start with learning from clean data and then gradually move to learn noisy-labeled data with pseudo labels produced by a time-ensemble of the model and data augmentations. Instead of using the instantaneous loss computed at the current step, our data selection is based on the dynamics of both the loss and output consistency for each sample across historical steps and different data augmentations, resulting in more precise detection of both clean labels and correct pseudo labels. On multiple benchmarks of noisy labels, we show that our curriculum learning strategy can significantly improve the test accuracy without any auxiliary model or extra clean data.

One-sentence Summary: RoCL improves noisy label learning by periodical transitions from supervised learning of clean labeled data to self-supervision of wrongly-labeled data, where the data are selected according to training dynamics."]]></description>
<dc:subject>curriculum-learning label-noise data-selection</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:beee41333f7f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:curriculum-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:label-noise"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:data-selection"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.12871">
    <title>[2104.12871] Why AI is Harder Than We Think</title>
    <dc:date>2021-04-28T15:50:30+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.12871</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment ("AI spring") and periods of disappointment, loss of confidence, and reduced funding ("AI winter"). Even with today's seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense."]]></description>
<dc:subject>ai machine-learning melanie-mitchell</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:dfbaab38dac8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:melanie-mitchell"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2006.13707">
    <title>[2006.13707] Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions</title>
    <dc:date>2021-04-22T15:00:09+00:00</dc:date>
    <link>https://arxiv.org/abs/2006.13707</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Recurrent neural networks (RNNs) are instrumental in modelling sequential and time-series data. Yet, when using RNNs to inform decision-making, predictions by themselves are not sufficient; we also need estimates of predictive uncertainty. Existing approaches for uncertainty quantification in RNNs are based predominantly on Bayesian methods; these are computationally prohibitive, and require major alterations to the RNN architecture and training. Capitalizing on ideas from classical jackknife resampling, we develop a frequentist alternative that: (a) does not interfere with model training or compromise its accuracy, (b) applies to any RNN architecture, and (c) provides theoretical coverage guarantees on the estimated uncertainty intervals. Our method derives predictive uncertainty from the variability of the (jackknife) sampling distribution of the RNN outputs, which is estimated by repeatedly deleting blocks of (temporally-correlated) training data, and collecting the predictions of the RNN re-trained on the remaining data. To avoid exhaustive re-training, we utilize influence functions to estimate the effect of removing training data blocks on the learned RNN parameters. Using data from a critical care setting, we demonstrate the utility of uncertainty quantification in sequential decision-making."]]></description>
<dc:subject>neural-net rnn uncertainty influence-function jackknife</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:09311fa8a025/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:neural-net"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:rnn"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:influence-function"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:jackknife"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.technologyreview.com/2021/04/13/1022568/big-tech-ai-ethics-guide/">
    <title>Big Tech’s guide to talking about AI ethics | MIT Technology Review</title>
    <dc:date>2021-04-15T20:03:15+00:00</dc:date>
    <link>https://www.technologyreview.com/2021/04/13/1022568/big-tech-ai-ethics-guide/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>ai ethics funny</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:a98557113597/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:ethics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:funny"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.00673">
    <title>[2104.00673] Cross-validation: what does it estimate and how well does it do it?</title>
    <dc:date>2021-04-02T15:03:09+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.00673</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Cross-validation is a widely-used technique to estimate prediction error, but its behavior is complex and not fully understood. Ideally, one would like to think that cross-validation estimates the prediction error for the model at hand, fit to the training data. We prove that this is not the case for the linear model fit by ordinary least squares; rather it estimates the average prediction error of models fit on other unseen training sets drawn from the same population. We further show that this phenomenon occurs for most popular estimates of prediction error, including data splitting, bootstrapping, and Mallow's Cp. Next, the standard confidence intervals for prediction error derived from cross-validation may have coverage far below the desired level. Because each data point is used for both training and testing, there are correlations among the measured accuracies for each fold, and so the usual estimate of variance is too small. We introduce a nested cross-validation scheme to estimate this variance more accurately, and show empirically that this modification leads to intervals with approximately correct coverage in many examples where traditional cross-validation intervals fail. Lastly, our analysis also shows that when producing confidence intervals for prediction accuracy with simple data splitting, one should not re-fit the model on the combined data, since this invalidates the confidence intervals."]]></description>
<dc:subject>cross-validation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:c90aa8c129e7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:cross-validation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/imurray/redirectify">
    <title>GitHub - imurray/redirectify: Browser extension to redirect pages based on rules. Intended to stop the browser following links straight to the PDF of a paper, but instead to go first to the HTML index page for the paper.</title>
    <dc:date>2021-03-29T12:03:48+00:00</dc:date>
    <link>https://github.com/imurray/redirectify</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Links to academic papers in emails and on websites often point to the PDF of the paper. However, on sites like arXiv, I'd much rather be pointed to the HTML page. The index page is quick to load, and has meta-data not available in the PDF, such as the version history. I've given up trying to ask people not to deep-link to PDFs, and have instead written a browser extension to do what I want."]]></description>
<dc:subject>firefox chrome extensions</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:ebdf3eebe9d0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:firefox"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:chrome"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:extensions"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2103.13318">
    <title>[2103.13318] Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types</title>
    <dc:date>2021-03-29T02:26:19+00:00</dc:date>
    <link>https://arxiv.org/abs/2103.13318</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["     Transfer learning enables to re-use knowledge learned on a source task to help learning a target task. A simple form of transfer learning is common in current state-of-the-art computer vision models, i.e. pre-training a model for image classification on the ILSVRC dataset, and then fine-tune on any target task. However, previous systematic studies of transfer learning have been limited and the circumstances in which it is expected to work are not fully understood. In this paper we carry out an extensive experimental exploration of transfer learning across vastly different image domains (consumer photos, autonomous driving, aerial imagery, underwater, indoor scenes, synthetic, close-ups) and task types (semantic segmentation, object detection, depth estimation, keypoint detection). Importantly, these are all complex, structured output tasks types relevant to modern computer vision applications. In total we carry out over 1200 transfer experiments, including many where the source and target come from different image domains, task types, or both. We systematically analyze these experiments to understand the impact of image domain, task type, and dataset size on transfer learning performance. Our study leads to several insights and concrete recommendations for practitioners. "]]></description>
<dc:subject>transfer-learning</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:e7aa5695f0fb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:transfer-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://anderson-review.ucla.edu/new-study-disavows-marshmallow-tests-predictive-powers/">
    <title>New Study Disavows Marshmallow Test's Predictive Powers - UCLA Anderson Review</title>
    <dc:date>2021-03-09T09:18:28+00:00</dc:date>
    <link>https://anderson-review.ucla.edu/new-study-disavows-marshmallow-tests-predictive-powers/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>psychology marshmallow education parenting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:ff003609ae57/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:marshmallow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:parenting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2103.00654">
    <title>[2103.00654] Feedback Coding for Active Learning</title>
    <dc:date>2021-03-06T01:47:21+00:00</dc:date>
    <link>https://arxiv.org/abs/2103.00654</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["The iterative selection of examples for labeling in active machine learning is conceptually similar to feedback channel coding in information theory: in both tasks, the objective is to seek a minimal sequence of actions to encode information in the presence of noise. While this high-level overlap has been previously noted, there remain open questions on how to best formulate active learning as a communications system to leverage existing analysis and algorithms in feedback coding. In this work, we formally identify and leverage the structural commonalities between the two problems, including the characterization of encoder and noisy channel components, to design a new algorithm. Specifically, we develop an optimal transport-based feedback coding scheme called Approximate Posterior Matching (APM) for the task of active example selection and explore its application to Bayesian logistic regression, a popular model in active learning. We evaluate APM on a variety of datasets and demonstrate learning performance comparable to existing active learning methods, at a reduced computational cost. These results demonstrate the potential of directly deploying concepts from feedback channel coding to design efficient active learning strategies."]]></description>
<dc:subject>active-learning information-theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:e17d2de17896/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:active-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:information-theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/nmstoker/gatherup">
    <title>GitHub - nmstoker/gatherup: Helps you post essential Python config details to GitHub or Discourse, all beautifully formatted</title>
    <dc:date>2021-03-02T18:17:59+00:00</dc:date>
    <link>https://github.com/nmstoker/gatherup</link>
    <dc:creator>arsyed</dc:creator><dc:subject>tools github issue bug reporting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:351cd92d8e25/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:github"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:issue"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:bug"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:reporting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2007.13241">
    <title>[2007.13241] Beyond the Worst-Case Analysis of Algorithms (Introduction)</title>
    <dc:date>2021-03-02T14:28:14+00:00</dc:date>
    <link>https://arxiv.org/abs/2007.13241</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["One of the primary goals of the mathematical analysis of algorithms is to provide guidance about which algorithm is the "best" for solving a given computational problem. Worst-case analysis summarizes the performance profile of an algorithm by its worst performance on any input of a given size, implicitly advocating for the algorithm with the best-possible worst-case performance. Strong worst-case guarantees are the holy grail of algorithm design, providing an application-agnostic certification of an algorithm's robustly good performance. However, for many fundamental problems and performance measures, such guarantees are impossible and a more nuanced analysis approach is called for. This chapter surveys several alternatives to worst-case analysis that are discussed in detail later in the book."

"Chapter 1 of the book Beyond the Worst-Case Analysis of Algorithms, edited by Tim Roughgarden and published by Cambridge University Press (2020"]]></description>
<dc:subject>books algorithms tim-roughgarden</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:dd53e99c085b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:tim-roughgarden"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://gallery.bridgesmathart.org/exhibitions/2016-bridges-conference/hans-kuiper">
    <title>Hans Kuiper &amp; Walt van Ballegooijen | Mathematical Art Galleries</title>
    <dc:date>2021-03-01T14:18:20+00:00</dc:date>
    <link>http://gallery.bridgesmathart.org/exhibitions/2016-bridges-conference/hans-kuiper</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Hans Kuiper and Walt van Ballegooijen created in cooperation this piece of art with the ‘hidden' faces of Gödel, Escher and Bach. We were inspired by the cover of Douglas Hofstadter’s book “Gödel, Escher Bach: an Eternal Golden Braid”. It shows an object, which projections show the three first letters of their names: a G, an E and a B. Our object shows their faces!

Our object is the result of the synthesis of two completely different Minimal Art techniques applied in a new type of Math Art.

The combined techniques are Minimal Art Objects and Optical Minimal Art Graphics.

The Minimal Art Object is based on the principles of a 16x16 Latin square. The object consists of 256 connected cubelets.

The faces are created by applying the technique of Optical Minimal Art Graphics: reducing colours, in this case grey colours are reduced to black and white.

The black colour is represented by the black structure, the white colour is the background which varies because of the shape of the holes in the cubes."]]></description>
<dc:subject>math art</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:77e3a73f092e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:math"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:art"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2102.01194">
    <title>[2102.01194] A Statistician Teaches Deep Learning</title>
    <dc:date>2021-02-05T22:07:04+00:00</dc:date>
    <link>https://arxiv.org/abs/2102.01194</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Deep learning (DL) has gained much attention and become increasingly popular in modern data science. Computer scientists led the way in developing deep learning techniques, so the ideas and perspectives can seem alien to statisticians. Nonetheless, it is important that statisticians become involved -- many of our students need this expertise for their careers. In this paper, developed as part of a program on DL held at the Statistical and Applied Mathematical Sciences Institute, we address this culture gap and provide tips on how to teach deep learning to statistics graduate students. After some background, we list ways in which DL and statistical perspectives differ, provide a recommended syllabus that evolved from teaching two iterations of a DL graduate course, offer examples of suggested homework assignments, give an annotated list of teaching resources, and discuss DL in the context of two research areas."]]></description>
<dc:subject>statistics neural-net samsi</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:a6652fcd3db8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:neural-net"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:samsi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://mjskay.github.io/ggdist/">
    <title>Visualizations of Distributions and Uncertainty • ggdist</title>
    <dc:date>2021-01-11T20:19:46+00:00</dc:date>
    <link>https://mjskay.github.io/ggdist/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>R libs ggplot visualization uncertainty distribution</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:a706cbec6f7a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:ggplot"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:distribution"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2006.06466">
    <title>[2006.06466] How Interpretable and Trustworthy are GAMs?</title>
    <dc:date>2021-01-05T18:00:28+00:00</dc:date>
    <link>https://arxiv.org/abs/2006.06466</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Generalized additive models (GAMs) have become a leading model class for data bias discovery and model auditing. However, there are a variety of algorithms for training GAMs, and these do not always learn the same things. Statisticians originally used splines to train GAMs, but more recently GAMs are being trained with boosted decision trees. It is unclear which GAM model(s) to believe, particularly when their explanations are contradictory. In this paper, we investigate a variety of different GAM algorithms both qualitatively and quantitatively on real and simulated datasets. Our results suggest that inductive bias plays a crucial role in model explanations and tree-based GAMs are to be recommended for the kinds of problems and dataset sizes we worked with."]]></description>
<dc:subject>gam</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:5206628ef63d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:gam"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://graph-tool.skewed.de/">
    <title>graph-tool: Efficent network analysis with python</title>
    <dc:date>2021-01-05T01:05:40+00:00</dc:date>
    <link>https://graph-tool.skewed.de/</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA[Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other Python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library. ]]></description>
<dc:subject>python libs graph networks visualization sna</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:b6b05a919c7b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:graph"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:sna"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://tenthousandmeters.com/tag/python-behind-the-scenes/">
    <title>Ten thousand meters - Python behind the scenes</title>
    <dc:date>2021-01-04T19:09:42+00:00</dc:date>
    <link>https://tenthousandmeters.com/tag/python-behind-the-scenes/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python cpython internals</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:e39868c75a69/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:cpython"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:internals"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/matthewfl/openfst-wrapper">
    <title>GitHub - matthewfl/openfst-wrapper</title>
    <dc:date>2021-01-02T15:11:00+00:00</dc:date>
    <link>https://github.com/matthewfl/openfst-wrapper</link>
    <dc:creator>arsyed</dc:creator><dc:subject>fst python libs</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:6a0ca3f82527/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:fst"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://newsboat.org/">
    <title>Newsboat, an RSS reader</title>
    <dc:date>2020-12-28T16:19:55+00:00</dc:date>
    <link>https://newsboat.org/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>software rss reader cli console</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:92802390676b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:software"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:rss"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:reader"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:cli"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:console"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ma.huji.ac.il/hart/papers/calib-minmax.pdf">
    <title>Calibrated Forecasts: The Minimax Proof</title>
    <dc:date>2020-12-21T19:46:09+00:00</dc:date>
    <link>http://www.ma.huji.ac.il/hart/papers/calib-minmax.pdf</link>
    <dc:creator>arsyed</dc:creator><dc:subject>calibration</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:2ef85a0e1e77/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:calibration"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/novoic/surfboard">
    <title>novoic/surfboard: Novoic's audio feature extraction library</title>
    <dc:date>2020-12-17T16:15:05+00:00</dc:date>
    <link>https://github.com/novoic/surfboard</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python libs audio feature-extraction</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:2f221bc1c5a9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:audio"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:feature-extraction"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/elements/text-analysis-in-python-for-social-scientists/BFAB0A3604C7E29F6198EA2F7941DFF3">
    <title>Text Analysis in Python for Social Scientists</title>
    <dc:date>2020-12-15T17:16:17+00:00</dc:date>
    <link>https://www.cambridge.org/core/elements/text-analysis-in-python-for-social-scientists/BFAB0A3604C7E29F6198EA2F7941DFF3</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA[Very excited to announce my first book "Text Analysis in Python for Social Scientists: Discovery and Exploration" has come out as an Element at @CambridgeUP!

Lots of executable Python code () and colorful pictures!]]></description>
<dc:subject>books text nlp social-science python data-analysis</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:a35882429d66/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:text"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:social-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:data-analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/satwikkansal/wtfpython">
    <title>GitHub - satwikkansal/wtfpython: What the f*ck Python?</title>
    <dc:date>2020-12-15T15:16:56+00:00</dc:date>
    <link>https://github.com/satwikkansal/wtfpython</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python gotchas tutorials</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:8911e797938c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:gotchas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:tutorials"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://news.ycombinator.com/item?id=25427504">
    <title>Legally Free Python Books List | Hacker News</title>
    <dc:date>2020-12-15T15:16:06+00:00</dc:date>
    <link>https://news.ycombinator.com/item?id=25427504</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python books free programming</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:0b3df0f4650a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:free"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:programming"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/content/112/25/7629">
    <title>Statistical learning and selective inference | PNAS</title>
    <dc:date>2020-11-30T14:10:54+00:00</dc:date>
    <link>https://www.pnas.org/content/112/25/7629</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA[We describe the problem of “selective inference.” This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have “cherry-picked”—searched for the strongest associations—means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.]]></description>
<dc:subject>statistics feature-selection lasso p-values inference</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:96dcda4f3ed2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:feature-selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:lasso"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:p-values"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://tmap.gdb.tools/">
    <title>tmap - Visualize big high-dimensional data</title>
    <dc:date>2020-11-25T19:58:00+00:00</dc:date>
    <link>https://tmap.gdb.tools/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python libs visualization big-data tree mst tmap</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:3c8a8a4c8af9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:big-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:tree"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:mst"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:tmap"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2008.01860">
    <title>[2008.01860] Importance of Self-Consistency in Active Learning for Semantic Segmentation</title>
    <dc:date>2020-11-23T20:40:00+00:00</dc:date>
    <link>https://arxiv.org/abs/2008.01860</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["We address the task of active learning in the context of semantic segmentation and show that self-consistency can be a powerful source of self-supervision to greatly improve the performance of a data-driven model with access to only a small amount of labeled data. Self-consistency uses the simple observation that the results of semantic segmentation for a specific image should not change under transformations like horizontal flipping (i.e., the results should only be flipped). In other words, the output of a model should be consistent under equivariant transformations. The self-supervisory signal of self-consistency is particularly helpful during active learning since the model is prone to overfitting when there is only a small amount of labeled training data. In our proposed active learning framework, we iteratively extract small image patches that need to be labeled, by selecting image patches that have high uncertainty (high entropy) under equivariant transformations. We enforce pixel-wise self-consistency between the outputs of segmentation network for each image and its transformation (horizontally flipped) to utilize the rich self-supervisory information and reduce the uncertainty of the network. In this way, we are able to find the image patches over which the current model struggles the most to classify. By iteratively training over these difficult image patches, our experiments show that our active learning approach reaches ∼96% of the top performance of a model trained on all data, by using only 12% of the total data on benchmark semantic segmentation datasets (e.g., CamVid and Cityscapes). "]]></description>
<dc:subject>active-learning self-consistency computer-vision</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:85e8b928f392/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:active-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:self-consistency"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:computer-vision"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/shreyashankar/create-ml-app">
    <title>GitHub - shreyashankar/create-ml-app: Template Makefile for ML projects in Python.</title>
    <dc:date>2020-11-23T20:04:49+00:00</dc:date>
    <link>https://github.com/shreyashankar/create-ml-app</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python machine-learning tools project</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:d680742b9381/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:project"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nickbuttrick.com/files/Advances2019.pdf">
    <title>The mind is its own place: The difficulties and benef its of thinking for pleasure</title>
    <dc:date>2020-11-22T21:14:21+00:00</dc:date>
    <link>https://www.nickbuttrick.com/files/Advances2019.pdf</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["This chapter is concerned with a type of thinking that has received little attention, namely intentional “thinking for pleasure”—the case in which people deliberately focus solely on their thoughts with the goal of generating positive affect. We present a model that describes why it is difficult to enjoy one's thoughts, how it can be done successfully, and when there is value in doing so. We review 36 studies we have conducted on this topic with just over 10,000 participants. We found that thinking for pleasure does not come easily to most people, but can be enjoyable and beneficial under the right conditions. Specifically, we found evidence that thinking for pleasure requires both motivation and the ability to concentrate. For example, several studies show that people enjoy thinking more when it is made easier with the use of “thinking aids.” We present evidence for a trade-off model that holds that people are most likely to enjoy their thoughts if they find those thoughts to be personally meaningful, but that such thinking involves concentration, which lowers enjoyment. Lastly, we review evidence for the benefits of thinking for pleasure, including an intervention study in which participants found thinking for pleasure enjoyable and meaningful in their everyday lives."]]></description>
<dc:subject>psychology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:cf5e6c2ee70d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:psychology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://twitter.com/jeremyphoward/status/1329436183655493632/photo/1">
    <title>Twitter</title>
    <dc:date>2020-11-19T14:47:40+00:00</dc:date>
    <link>https://twitter.com/jeremyphoward/status/1329436183655493632/photo/1</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA[Want to quickly edit or view the source of any Python module? Just define these 2 bash functions.

pyvim() { vim $(python -c "import ${1} as o; print(o.__file__)"); }
pyshow() { pygmentize $(python -c "import ${1} as o; print(o.__file__)"); } ]]></description>
<dc:subject>python cli source tips</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:7dd7489d1979/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:cli"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:source"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:tips"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/breandan/kotlingrad">
    <title>GitHub - breandan/kotlingrad: Shape-Safe Symbolic Differentiation with Algebraic Data Types</title>
    <dc:date>2020-11-18T19:19:12+00:00</dc:date>
    <link>https://github.com/breandan/kotlingrad</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Kotlin∇ is a type-safe automatic differentiation framework in Kotlin. It allows users to express differentiable programs with higher-dimensional data structures and operators. We attempt to restrict syntactically valid constructions to those which are algebraically valid and can be checked at compile-time. By enforcing these constraints in the type system, it eliminates certain classes of runtime errors that may occur during the execution of a differentiable program. Due to type-inference, most type declarations may be safely omitted by the end user. Kotlin∇ strives to be expressive, safe, and notationally similar to mathematics. It is currently pre-release and offers no stability guarantees at this time."]]></description>
<dc:subject>kotlin differentiable-programming</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:d84071480fc6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:kotlin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:differentiable-programming"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ai.facebook.com/blog/paving-the-way-for-software-20-with-kotlin/">
    <title>Paving the way for Software 2.0 with Kotlin</title>
    <dc:date>2020-11-18T15:19:59+00:00</dc:date>
    <link>https://ai.facebook.com/blog/paving-the-way-for-software-20-with-kotlin/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>kotlin proglang differentiable-programming</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:68e274998986/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:kotlin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:proglang"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:differentiable-programming"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/fabiopardo/tonic">
    <title>fabiopardo/tonic: Tonic RL library</title>
    <dc:date>2020-11-17T16:15:05+00:00</dc:date>
    <link>https://github.com/fabiopardo/tonic</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python pytorch libs reinforcement-learning tensorflow</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:ff88814c2f9e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:pytorch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:reinforcement-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:tensorflow"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://stackoverflow.com/questions/50766461/whats-the-difference-between-namedtuple-and-namedtuple/50767206#50767206">
    <title>What's the difference between namedtuple and NamedTuple? - Stack Overflow</title>
    <dc:date>2020-11-10T13:36:41+00:00</dc:date>
    <link>https://stackoverflow.com/questions/50766461/whats-the-difference-between-namedtuple-and-namedtuple/50767206#50767206</link>
    <dc:creator>arsyed</dc:creator><dc:subject>types namedtuple python</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:19bd47fe581c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:types"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:namedtuple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://stackoverflow.com/questions/60430110/python-typing-support-for-namedtuple/60430640#60430640">
    <title>Python typing support for NamedTuple - Stack Overflow</title>
    <dc:date>2020-11-10T13:35:26+00:00</dc:date>
    <link>https://stackoverflow.com/questions/60430110/python-typing-support-for-namedtuple/60430640#60430640</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python gotchas metaclass namedtuple types</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:fd5984852006/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:gotchas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:metaclass"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:namedtuple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:types"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/elements/american-affective-polarization-in-comparative-perspective/1E3584B482D51DB25FFFB37A8044F204/core-reader#">
    <title>American Affective Polarization in Comparative Perspective</title>
    <dc:date>2020-11-09T21:10:00+00:00</dc:date>
    <link>https://www.cambridge.org/core/elements/american-affective-polarization-in-comparative-perspective/1E3584B482D51DB25FFFB37A8044F204/core-reader#</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["American political observers express increasing concern about affective polarization, i.e., partisans' resentment toward political opponents. We advance debates about America's partisan divisions by comparing affective polarization in the US over the past 25 years with affective polarization in 19 other western publics. We conclude that American affective polarization is not extreme in comparative perspective, although Americans' dislike of partisan opponents has increased more rapidly since the mid-1990s than in most other Western publics. We then show that affective polarization is more intense when unemployment and inequality are high; when political elites clash over cultural issues such as immigration and national identity; and in countries with majoritarian electoral institutions. Our findings situate American partisan resentment and hostility in comparative perspective, and illuminate correlates of affective polarization that are difficult to detect when examining the American case in isolation."]]></description>
<dc:subject>politics polarization culture-wars</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:434b011d1c54/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:polarization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:culture-wars"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://nbdev.fast.ai/">
    <title>Welcome to nbdev | nbdev</title>
    <dc:date>2020-11-09T21:03:33+00:00</dc:date>
    <link>https://nbdev.fast.ai/</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA[nbdev is a library that allows you to develop a python library in Jupyter Notebooks, putting all your code, tests and documentation in one place. That is: you now have a true literate programming environment, as envisioned by Donald Knuth back in 1983!]]></description>
<dc:subject>python libs jupyter documentation library packaging</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:008e05c54c99/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:jupyter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:documentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:library"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:packaging"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://rise.readthedocs.io/en/stable/">
    <title>RISE — RISE 5.7.1</title>
    <dc:date>2020-11-05T23:39:48+00:00</dc:date>
    <link>https://rise.readthedocs.io/en/stable/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python jupyter slideshow presentation .checkout</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:6e830b2f9a00/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:jupyter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:slideshow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:presentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:.checkout"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/santosjorge/cufflinks">
    <title>GitHub - santosjorge/cufflinks: Productivity Tools for Plotly + Pandas</title>
    <dc:date>2020-11-05T21:08:03+00:00</dc:date>
    <link>https://github.com/santosjorge/cufflinks</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA[This library binds the power of plotly with the flexibility of pandas for easy plotting.]]></description>
<dc:subject>python libs plotly pandas visualization plotting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:542250332c1f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:plotly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:pandas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:plotting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/pandas-profiling/pandas-profiling">
    <title>GitHub - pandas-profiling/pandas-profiling: Create HTML profiling reports from pandas DataFrame objects</title>
    <dc:date>2020-11-05T21:06:45+00:00</dc:date>
    <link>https://github.com/pandas-profiling/pandas-profiling</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Generates profile reports from a pandas DataFrame.

The pandas df.describe() function is great but a little basic for serious exploratory data analysis. pandas_profiling extends the pandas DataFrame with df.profile_report() for quick data analysis.

For each column the following statistics - if relevant for the column type - are presented in an interactive HTML report:"]]></description>
<dc:subject>python pandas libs summary eda reporting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:ba2cadb82de9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:pandas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:summary"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:eda"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:reporting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://gist.github.com/jph00/d5981f649a83a754946964cf22322cb2">
    <title>Organized and hyperlinked index to every module, function, and class in the Python standard library</title>
    <dc:date>2020-11-03T20:05:23+00:00</dc:date>
    <link>https://gist.github.com/jph00/d5981f649a83a754946964cf22322cb2</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python stdlib libs docs</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:8c1622bca92e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:stdlib"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:docs"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.scientificamerican.com/article/what-nasa-could-teach-tesla-about-autopilot-s-limits/">
    <title>What NASA Could Teach Tesla about Autopilot's Limits - Scientific American</title>
    <dc:date>2020-10-27T15:09:53+00:00</dc:date>
    <link>https://www.scientificamerican.com/article/what-nasa-could-teach-tesla-about-autopilot-s-limits/</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["In other words, the everyday driving environment affords so little margin for error that any distinction between “on” and “in” the loop can quickly become moot."]]></description>
<dc:subject>autonomous-driving ooda</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:0f4fc6a8dc40/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:autonomous-driving"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:ooda"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://p403n1x87.github.io/the-austin-tui-way-to-resourceful-text-based-user-interfaces.html">
    <title>The Hub of Heliopolis - The Austin TUI Way to Resourceful Text-based User Interfaces</title>
    <dc:date>2020-10-27T14:37:08+00:00</dc:date>
    <link>https://p403n1x87.github.io/the-austin-tui-way-to-resourceful-text-based-user-interfaces.html</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python cli ui tui curses</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:c641e97ce3ae/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:cli"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:ui"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:tui"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:curses"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/P403n1x87/austin">
    <title>GitHub - P403n1x87/austin: Python frame stack sampler for CPython</title>
    <dc:date>2020-10-27T14:24:47+00:00</dc:date>
    <link>https://github.com/P403n1x87/austin</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python profiler</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:427835f2fc5c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:profiler"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/P403n1x87/austin-tui">
    <title>GitHub - P403n1x87/austin-tui: The top-like text-based user interface for Austin</title>
    <dc:date>2020-10-27T14:20:59+00:00</dc:date>
    <link>https://github.com/P403n1x87/austin-tui</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python profiler</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:601eb0e59cca/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:profiler"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://news.ycombinator.com/item?id=24906237">
    <title>Spy inside a running Python application at no performance cost with Austin TUI | Hacker News</title>
    <dc:date>2020-10-27T14:12:33+00:00</dc:date>
    <link>https://news.ycombinator.com/item?id=24906237</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python profiling spy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:8f20c80ee915/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:profiling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:spy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/cosmologicon/maff">
    <title>GitHub - cosmologicon/maff: Python convenience functions that I wish were in the math module</title>
    <dc:date>2020-10-24T18:48:10+00:00</dc:date>
    <link>https://github.com/cosmologicon/maff</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python libs math functions utils</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:8a8b5762bc56/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:math"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:functions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:utils"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/cosmologicon/grf">
    <title>GitHub - cosmologicon/grf: simple tools for solving graph problems in python</title>
    <dc:date>2020-10-24T18:33:26+00:00</dc:date>
    <link>https://github.com/cosmologicon/grf</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Currently implemented:
    Hamiltonian path and cycle
    Exact cover
    A* path finding"]]></description>
<dc:subject>python code graph algorithms a*</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:e13deb4c770d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:code"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:graph"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:a*"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://pypi.org/project/logutils/">
    <title>logutils · PyPI</title>
    <dc:date>2020-10-24T17:58:28+00:00</dc:date>
    <link>https://pypi.org/project/logutils/</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA[The logutils package provides a set of handlers for the Python standard library’s logging package.

Some of these handlers are out-of-scope for the standard library, and so they are packaged here. Others are updated versions which have appeared in recent Python releases, but are usable with older versions of Python and so are packaged here.]]></description>
<dc:subject>python libs logging utils</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:e64fb580cd68/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:logging"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:utils"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2010.06595">
    <title>[2010.06595] With Little Power Comes Great Responsibility</title>
    <dc:date>2020-10-22T16:17:48+00:00</dc:date>
    <link>https://arxiv.org/abs/2010.06595</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Despite its importance to experimental design, statistical power (the probability that, given a real effect, an experiment will reject the null hypothesis) has largely been ignored by the NLP community. Underpowered experiments make it more difficult to discern the difference between statistical noise and meaningful model improvements, and increase the chances of exaggerated findings. By meta-analyzing a set of existing NLP papers and datasets, we characterize typical power for a variety of settings and conclude that underpowered experiments are common in the NLP literature. In particular, for several tasks in the popular GLUE benchmark, small test sets mean that most attempted comparisons to state of the art models will not be adequately powered. Similarly, based on reasonable assumptions, we find that the most typical experimental design for human rating studies will be underpowered to detect small model differences, of the sort that are frequently studied. For machine translation, we find that typical test sets of 2000 sentences have approximately 75% power to detect differences of 1 BLEU point. To improve the situation going forward, we give an overview of best practices for power analysis in NLP and release a series of notebooks to assist with future power analyses."]]></description>
<dc:subject>statistics power nlp machine-learning dan-jurafsky</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:c7fb25e8f766/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:power"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:dan-jurafsky"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2009.12820">
    <title>[2009.12820] Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning</title>
    <dc:date>2020-10-19T20:50:24+00:00</dc:date>
    <link>https://arxiv.org/abs/2009.12820</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["The impressive performance exhibited by modern machine learning models hinges on the ability to train such models on a very large amounts of labeled data. However, since access to large volumes of labeled data is often limited or expensive, it is desirable to alleviate this bottleneck by carefully curating the training set. Optimal experimental design is a well-established paradigm for selecting data point to be labeled so to maximally inform the learning process. Unfortunately, classical theory on optimal experimental design focuses on selecting examples in order to learn underparameterized (and thus, non-interpolative) models, while modern machine learning models such as deep neural networks are overparameterized, and oftentimes are trained to be interpolative. As such, classical experimental design methods are not applicable in many modern learning setups. Indeed, the predictive performance of underparameterized models tends to be variance dominated, so classical experimental design focuses on variance reduction, while the predictive performance of overparameterized models can also be, as is shown in this paper, bias dominated or of mixed nature. In this paper we propose a design strategy that is well suited for overparameterized regression and interpolation, and we demonstrate the applicability of our method in the context of deep learning by proposing a new algorithm for single-shot deep active learning."]]></description>
<dc:subject>active-learning neural-net experiment-design</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:7622c6b8051d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:active-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:neural-net"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:experiment-design"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1905.12782">
    <title>[1905.12782] MaxiMin Active Learning in Overparameterized Model Classes</title>
    <dc:date>2020-10-19T20:47:53+00:00</dc:date>
    <link>https://arxiv.org/abs/1905.12782</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Generating labeled training datasets has become a major bottleneck in Machine Learning (ML) pipelines. Active ML aims to address this issue by designing learning algorithms that automatically and adaptively select the most informative examples for labeling so that human time is not wasted labeling irrelevant, redundant, or trivial examples. This paper proposes a new approach to active ML with nonparametric or overparameterized models such as kernel methods and neural networks. In the context of binary classification, the new approach is shown to possess a variety of desirable properties that allow active learning algorithms to automatically and efficiently identify decision boundaries and data clusters."]]></description>
<dc:subject>active-learning neural-net kernel-methods</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:e8acff61c075/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:active-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:neural-net"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:kernel-methods"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2002.11097">
    <title>[2002.11097] Problems with Shapley-value-based explanations as feature importance measures</title>
    <dc:date>2020-10-16T19:25:48+00:00</dc:date>
    <link>https://arxiv.org/abs/2002.11097</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA[ Game-theoretic formulations of feature importance have become popular as a way to "explain" machine learning models. These methods define a cooperative game between the features of a model and distribute influence among these input elements using some form of the game's unique Shapley values. Justification for these methods rests on two pillars: their desirable mathematical properties, and their applicability to specific motivations for explanations. We show that mathematical problems arise when Shapley values are used for feature importance and that the solutions to mitigate these necessarily induce further complexity, such as the need for causal reasoning. We also draw on additional literature to argue that Shapley values do not provide explanations which suit human-centric goals of explainability. ]]></description>
<dc:subject>shapley feature-importance explanation critique interpretability</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:1e7596661785/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:shapley"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:feature-importance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:critique"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:interpretability"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html">
    <title>Speech Processing for Machine Learning: Filter banks, Mel-Frequency Cepstral Coefficients (MFCCs) and What’s In-Between | Haytham Fayek</title>
    <dc:date>2020-10-15T22:37:55+00:00</dc:date>
    <link>https://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html</link>
    <dc:creator>arsyed</dc:creator><dc:subject>speech feature-extraction mfcc dsp python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:a15bcfb9221c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:speech"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:feature-extraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:mfcc"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:dsp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://pysdr.org/index.html">
    <title>PySDR: A Guide to SDR and DSP using Python — PySDR: A Guide to SDR and DSP using Python 0.1 documentation</title>
    <dc:date>2020-10-12T04:47:41+00:00</dc:date>
    <link>https://pysdr.org/index.html</link>
    <dc:creator>arsyed</dc:creator><dc:subject>books dsp python radio sdr .learn libs</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:f8673a17fe81/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:dsp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:radio"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:sdr"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:.learn"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://greenteapress.com/wp/think-dsp/">
    <title>Think DSP – Green Tea Press</title>
    <dc:date>2020-10-12T04:47:22+00:00</dc:date>
    <link>https://greenteapress.com/wp/think-dsp/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>books dsp python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:7bca96c1a206/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:dsp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
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<item rdf:about="https://github.com/bnpy/bnpy">
    <title>GitHub - bnpy/bnpy: Bayesian nonparametric machine learning for Python</title>
    <dc:date>2020-10-11T20:07:45+00:00</dc:date>
    <link>https://github.com/bnpy/bnpy</link>
    <dc:creator>arsyed</dc:creator><dc:subject>python libs bayesian nonparametric machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:15ad93e15c54/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:libs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:bayesian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:nonparametric"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:machine-learning"/>
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<item rdf:about="https://mitpress.mit.edu/books/matter-facts">
    <title>The Matter of Facts | The MIT Press</title>
    <dc:date>2020-10-10T03:05:29+00:00</dc:date>
    <link>https://mitpress.mit.edu/books/matter-facts</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["How biases, the desire for a good narrative, reliance on citation metrics, and other problems undermine confidence in modern science.

Modern science is built on experimental evidence, yet scientists are often very selective in deciding what evidence to use and tend to disagree about how to interpret it. In The Matter of Facts, Gareth and Rhodri Leng explore how scientists produce and use evidence. They do so to contextualize an array of problems confronting modern science that have raised concerns about its reliability: the widespread use of inappropriate statistical tests, a shortage of replication studies, and a bias in both publishing and citing “positive” results. Before these problems can be addressed meaningfully, the authors argue, we must understand what makes science work and what leads it astray.

The myth of science is that scientists constantly challenge their own thinking. But in reality, all scientists are in the business of persuading other scientists of the importance of their own ideas, and they do so by combining reason with rhetoric. Often, they look for evidence that will support their ideas, not for evidence that might contradict them; often, they present evidence in a way that makes it appear to be supportive; and often, they ignore inconvenient evidence.

In a series of essays focusing on controversies, disputes, and discoveries, the authors vividly portray science as a human activity, driven by passion as well as by reason. By analyzing the fluidity of scientific concepts and the dynamic and unpredictable development of scientific fields, the authors paint a picture of modern science and the pressures it faces."]]></description>
<dc:subject>books science sociology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:5dbdbb736cd7/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:sociology"/>
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