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    <title>MLX — MLX 0.0.9 documentation</title>
    <dc:date>2024-01-18T21:55:02+00:00</dc:date>
    <link>https://ml-explore.github.io/mlx/build/html/index.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[MLX is a NumPy-like array framework designed for efficient and flexible machine learning on Apple silicon, brought to you by Apple machine learning research.]]></description>
<dc:subject>python machinelearning macos</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:ce400c42740e/</dc:identifier>
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<item rdf:about="https://madebyoll.in/posts/dino_diffusion/">
    <title>Bare-bones Diffusion Models</title>
    <dc:date>2023-02-09T17:00:14+00:00</dc:date>
    <link>https://madebyoll.in/posts/dino_diffusion/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[To understand how diffusion models generate images, I wrote code for training a bare-bones diffusion model without all the fanciness (no “epsilon parameterization”, no “Gaussian conditionals”, no sqrt_one_minus_alphas_cumprod).

Based on that code, I trained a tiny diffusion model that generates 512×512 botanical images in your web browser.]]></description>
<dc:subject>machinelearning tutorials</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:09921455e35d/</dc:identifier>
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<item rdf:about="https://twitter.com/karpathy/status/1468370605229547522">
    <title>Andrej Karpathy on Twitter: &quot;The ongoing consolidation in AI is incredible. Thread: ➡️ When I started ~decade ago vision, speech, natural language, reinforcement learning, etc. were completely separate; You couldn't read papers across areas - the appr</title>
    <dc:date>2022-11-23T22:33:01+00:00</dc:date>
    <link>https://twitter.com/karpathy/status/1468370605229547522</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[But as of approx. last two years, even the neural net architectures across all areas are starting to look identical - a Transformer (definable in ~200 lines of PyTorch https://github.com/karpathy/minGPT/blob/master/mingpt/model.py…), with very minor differences. Either as a strong baseline or (often) state of the art.]]></description>
<dc:subject>machinelearning transformer</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:e0f7e400c2c5/</dc:identifier>
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<item rdf:about="https://data-science-blog.com/blog/2020/12/30/transformer/">
    <title>Instructions on Transformer for people outside NLP field, but with examples of NLP - Data Science Blog</title>
    <dc:date>2022-11-23T22:31:54+00:00</dc:date>
    <link>https://data-science-blog.com/blog/2020/12/30/transformer/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In this article series, I am going to provide explanations on minimum prerequisites for understanding deep learning in NLP (natural language process) tasks, but NLP is not the main focus of this article series, and actually I do not study in NLP field. I think Transformer is going to be a new major model of deep learning as well as CNN or RNN, and the model is now being applied in various fields.

]]></description>
<dc:subject>transformer machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:c14645c99f10/</dc:identifier>
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    <title>Transformer Architecture: The Positional Encoding - Amirhossein Kazemnejad's Blog</title>
    <dc:date>2022-11-23T22:29:16+00:00</dc:date>
    <link>https://kazemnejad.com/blog/transformer_architecture_positional_encoding/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Transformer architecture was introduced as a novel pure attention-only sequence-to-sequence architecture by Vaswani et al. Its ability for parallelizable training and its general performance improvement made it a popular option among NLP (and recently CV) researchers.

Thanks to the several implementations in common deep learning frameworks, it became an easy option to experiment with for many students (including myself). Even though making it more accessible is a great thing, but on the downside it may cause the details of the model to be ignored.

In this article, I don’t plan to explain its architecture in depth as there are currently several great tutorials on this topic (here, here, and here), but alternatively, I want to discuss one specific part of the transformer’s architecture - the positional encoding.]]></description>
<dc:subject>attention transformer nlp machinelearning</dc:subject>
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    <title>The Annotated Transformer</title>
    <dc:date>2022-11-23T22:26:36+00:00</dc:date>
    <link>http://nlp.seas.harvard.edu/annotated-transformer/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The Transformer has been on a lot of people’s minds over the last year five years. This post presents an annotated version of the paper in the form of a line-by-line implementation. It reorders and deletes some sections from the original paper and adds comments throughout. This document itself is a working notebook, and should be a completely usable implementation. Code is available here.

]]></description>
<dc:subject>machinelearning transformer</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:c9477dfb951d/</dc:identifier>
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<item rdf:about="http://jalammar.github.io/illustrated-transformer/">
    <title>The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time.</title>
    <dc:date>2022-11-23T22:24:44+00:00</dc:date>
    <link>http://jalammar.github.io/illustrated-transformer/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In the previous post, we looked at Attention – a ubiquitous method in modern deep learning models. Attention is a concept that helped improve the performance of neural machine translation applications. In this post, we will look at The Transformer – a model that uses attention to boost the speed with which these models can be trained. The Transformer outperforms the Google Neural Machine Translation model in specific tasks. The biggest benefit, however, comes from how The Transformer lends itself to parallelization. It is in fact Google Cloud’s recommendation to use The Transformer as a reference model to use their Cloud TPU offering. So let’s try to break the model apart and look at how it functions.

]]></description>
<dc:subject>nlp machinelearning transformer</dc:subject>
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    <title>Illustrated Guide to Transformers- Step by Step Explanation | by Michael Phi | Towards Data Science</title>
    <dc:date>2022-11-23T22:24:30+00:00</dc:date>
    <link>https://towardsdatascience.com/illustrated-guide-to-transformers-step-by-step-explanation-f74876522bc0</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Transformers are taking the natural language processing world by storm. These incredible models are breaking multiple NLP records and pushing the state of the art. They are used in many applications like machine language translation, conversational chatbots, and even to power better search engines. Transformers are the rage in deep learning nowadays, but how do they work? Why have they outperform the previous king of sequence problems, like recurrent neural networks, GRU’s, and LSTM’s? You’ve probably heard of different famous transformers models like BERT, GPT, and GPT2. In this post, we’ll focus on the one paper that started it all, “Attention is all you need”.
]]></description>
<dc:subject>nlp machinelearning transformer</dc:subject>
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<item rdf:about="https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial2/Introduction_to_PyTorch.html">
    <title>Tutorial 2: Introduction to PyTorch — UvA DL Notebooks v1.2 documentation</title>
    <dc:date>2022-11-23T22:24:06+00:00</dc:date>
    <link>https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial2/Introduction_to_PyTorch.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Welcome to our PyTorch tutorial for the Deep Learning course 2022 at the University of Amsterdam! The following notebook is meant to give a short introduction to PyTorch basics, and get you setup for writing your own neural networks.]]></description>
<dc:subject>pytorch tutorial machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://d2l.ai/chapter_preface/index.html">
    <title>Preface — Dive into Deep Learning 1.0.0-alpha1.post0 documentation</title>
    <dc:date>2022-11-23T22:23:40+00:00</dc:date>
    <link>https://d2l.ai/chapter_preface/index.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code.

]]></description>
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<item rdf:about="https://pytorch.org/tutorials/beginner/basics/intro.html">
    <title>Learn the Basics — PyTorch Tutorials 1.12.1+cu102 documentation</title>
    <dc:date>2022-11-23T22:23:21+00:00</dc:date>
    <link>https://pytorch.org/tutorials/beginner/basics/intro.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Most machine learning workflows involve working with data, creating models, optimizing model parameters, and saving the trained models. This tutorial introduces you to a complete ML workflow implemented in PyTorch, with links to learn more about each of these concepts.

We’ll use the FashionMNIST dataset to train a neural network that predicts if an input image belongs to one of the following classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, or Ankle boot.]]></description>
<dc:subject>pytorch tutorials machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://dmg.org/pfa/">
    <title>PFA · Portable Format for Analytics</title>
    <dc:date>2022-10-09T20:16:35+00:00</dc:date>
    <link>https://dmg.org/pfa/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[PFA is an open standard for statistical models, machine learning models and data transformation engines. PFA combines the ease of portability across systems with algorithmic flexibility: models, pre-processing, and post-processing are all functions that can be arbitrarily composed, chained, or built into complex workflows. PFA may be as simple as a raw data transformation or as sophisticated as advanced ML/AI algorithms, all described as a JSON or YAML configuration file.]]></description>
<dc:subject>machinelearning statistics metadata standards</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://course.fast.ai/">
    <title>Practical Deep Learning for Coders - Practical Deep Learning</title>
    <dc:date>2022-08-10T21:47:57+00:00</dc:date>
    <link>https://course.fast.ai/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.]]></description>
<dc:subject>machinelearning ai course</dc:subject>
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<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:course"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://distill.pub/2021/gnn-intro/">
    <title>A Gentle Introduction to Graph Neural Networks</title>
    <dc:date>2022-06-03T20:32:08+00:00</dc:date>
    <link>https://distill.pub/2021/gnn-intro/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Neural networks have been adapted to leverage the structure and properties of graphs. We explore the components needed for building a graph neural network - and motivate the design choices behind them.]]></description>
<dc:subject>graphs machinelearning tutorial neural</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:d52411b218ff/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:graphs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:neural"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/facebookresearch/metaseq/tree/main/projects/OPT/chronicles">
    <title>metaseq/projects/OPT/chronicles at main · facebookresearch/metaseq</title>
    <dc:date>2022-05-10T00:39:56+00:00</dc:date>
    <link>https://github.com/facebookresearch/metaseq/tree/main/projects/OPT/chronicles</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Here we have included our full logbook used while training the OPT-175B model, along with a series of notes written to summarize the process and communicate some of the challenges we faced along the way.]]></description>
<dc:subject>machinelearning documentation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:fa98d3744f28/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:documentation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://pair.withgoogle.com/explorables/dataset-worldviews/">
    <title>Datasets Have Worldviews</title>
    <dc:date>2022-03-18T19:47:55+00:00</dc:date>
    <link>https://pair.withgoogle.com/explorables/dataset-worldviews/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Every dataset communicates a different perspective. When you shift your perspective, your conclusions can shift, too.]]></description>
<dc:subject>bias machinelearning inls201</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:f8c33dee0127/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:bias"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls201"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://statmodeling.stat.columbia.edu/2021/07/07/top-10-ideas-in-statistics-that-have-powered-the-ai-revolution/">
    <title>Top 10 Ideas in Statistics That Have Powered the AI Revolution « Statistical Modeling, Causal Inference, and Social Science</title>
    <dc:date>2021-07-07T17:37:55+00:00</dc:date>
    <link>https://statmodeling.stat.columbia.edu/2021/07/07/top-10-ideas-in-statistics-that-have-powered-the-ai-revolution/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Each idea below can be viewed as a stand-in for an entire subfield. We make no claim that these are the “best” articles and books in statistics and machine learning, we’re just saying they’re important in themselves and represent important developments. By singling out these works, we do not mean to diminish the importance of similar, related work. We focus on methods in statistics and machine learning, rather than equally important breakthroughs in statistical computing, and computer science and engineering, which have provided the tools and computing power for data analysis and visualization to become everyday practical tools. Finally, we have focused on methods, while recognizing that developments in theory and methods are often motivated by specific applications.]]></description>
<dc:subject>statistics AI machinelearning history</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:7c05799c15b7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:AI"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:history"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://towardsdatascience.com/10-overlooked-machine-learning-advances-in-the-last-10-decades-2e9fe9f2f073">
    <title>10 Overlooked Machine Learning Advances in the Last 10 Decades</title>
    <dc:date>2020-01-14T01:07:58+00:00</dc:date>
    <link>https://towardsdatascience.com/10-overlooked-machine-learning-advances-in-the-last-10-decades-2e9fe9f2f073</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Machine Learning today is built on almost a century of advances in science and technology. Some of the most important advances were under-appreciated at the time and most remain overlooked today. This article shares one overlooked advance in machine learning from each of the last 10 decades.
The most famous advances in AI and machine learning have already received too much attention and arguing about them will amplify that bias. By comparison, it is much more interesting to look at what was overlooked. With that in mind, this article characterize the machine learning trend for each decade and identifies one important but overlooked development for that decade.]]></description>
<dc:subject>machinelearning history</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:62b534d399d0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:history"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://teachablemachine.withgoogle.com/">
    <title>Teachable Machine</title>
    <dc:date>2019-11-24T21:58:55+00:00</dc:date>
    <link>https://teachablemachine.withgoogle.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Train a computer to recognize your own images, sounds, & poses.

A fast, easy way to create machine learning models for your sites, apps, and more – no expertise or coding required.]]></description>
<dc:subject>machinelearning classification demo inls201</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:1337413612e3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:demo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls201"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://twitter.com/i/web/status/1115813185968267264">
    <title>Twitter</title>
    <dc:date>2019-04-11T07:17:06+00:00</dc:date>
    <link>https://twitter.com/i/web/status/1115813185968267264</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[RT @chadloder: TW: racist and homophobic slurs.

I've just finished some very sophisticated #MachineLearning and #AI analysis whic… ]]></description>
<dc:subject>AI MachineLearning</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:08aebe080d5e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:AI"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://course.fast.ai/">
    <title>Practical Deep Learning For Coders—18 hours of lessons for free</title>
    <dc:date>2017-02-04T18:24:44+00:00</dc:date>
    <link>http://course.fast.ai/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[fast.ai's 7 week course, Practical Deep Learning For Coders, Part 1, taught by Jeremy Howard (Kaggle's #1 competitor 2 years running, and founder of Enlitic). Learn how to build state of the art models without needing graduate-level math—but also without dumbing anything down.]]></description>
<dc:subject>machinelearning course python keras theano</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:8bfd174f01c3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:course"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:theano"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://iamtrask.github.io/2015/07/12/basic-python-network/">
    <title>A Neural Network in 11 lines of Python (Part 1) - i am trask</title>
    <dc:date>2016-03-03T22:32:47+00:00</dc:date>
    <link>http://iamtrask.github.io/2015/07/12/basic-python-network/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This tutorial teaches backpropagation via a very simple toy example, a short python implementation.]]></description>
<dc:subject>machinelearning python tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:a4fb52019bbb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tensorflow.org/versions/master/tutorials/mnist/beginners/index.html#mnist-for-ml-beginners">
    <title>TensorFlow -- MNIST For ML Beginners</title>
    <dc:date>2016-01-13T13:58:25+00:00</dc:date>
    <link>https://www.tensorflow.org/versions/master/tutorials/mnist/beginners/index.html#mnist-for-ml-beginners</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In this tutorial, we're going to train a model to look at images and predict what digits they are. Our goal isn't to train a really elaborate model that achieves state-of-the-art performance -- although we'll give you code to do that later! -- but rather to dip a toe into using TensorFlow. As such, we're going to start with a very simple model, called a Softmax Regression.

The actual code for this tutorial is very short, and all the interesting stuff happens in just three lines. However, it is very important to understand the ideas behind it: both how TensorFlow works and the core machine learning concepts. Because of this, we are going to very carefully work through the code.]]></description>
<dc:subject>machinelearning tensorflow tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:21f1f8943d7e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://karpathy.github.io/2015/05/21/rnn-effectiveness/">
    <title>The Unreasonable Effectiveness of Recurrent Neural Networks</title>
    <dc:date>2015-12-09T20:01:00+00:00</dc:date>
    <link>http://karpathy.github.io/2015/05/21/rnn-effectiveness/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[There's something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simple your model is to the quality of the results you get out of it blows past your expectations, and this was one of those times. What made this result so shocking at the time was that the common wisdom was that RNNs were supposed to be difficult to train (with more experience I've in fact reached the opposite conclusion). Fast forward about a year: I'm training RNNs all the time and I've witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me. This post is about sharing some of that magic with you.]]></description>
<dc:subject>ai machinelearning deeplearning datastudies</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:05f330370e5c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datastudies"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://blog.wikimedia.org/2015/11/30/artificial-intelligence-x-ray-specs/">
    <title>Artificial intelligence service gives Wikipedians ‘X-ray specs’ to see through bad edits « Wikimedia blog</title>
    <dc:date>2015-12-02T20:00:20+00:00</dc:date>
    <link>https://blog.wikimedia.org/2015/11/30/artificial-intelligence-x-ray-specs/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Wikipedia is edited about half a million times per day. In order to maintain the quality of Wikipedia, this firehose of new content needs to be constantly reviewed by Wikipedians. The Objective Revision Evaluation Service (ORES) functions like a pair of X-ray specs, the toy hyped in novelty shops and the back of comic books—but these specs actually work to highlight potentially damaging edits for editors. This allows editors to triage them from the torrent of new edits and review them with increased scrutiny.
By combining open data and open source machine learning algorithms, our goal is to make quality control in Wikipedia more transparent, auditable, and easy to experiment with.
Our hope is that ORES will enable critical advancements in how we do quality control—changes that will both make quality control work more efficient and make Wikipedia a more welcoming place for new editors.]]></description>
<dc:subject>ai wikipedia editing machinelearning datastudies</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:310d3d88c861/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:wikipedia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:editing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datastudies"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/karpathy/char-rnn">
    <title>karpathy/char-rnn</title>
    <dc:date>2015-10-14T12:46:43+00:00</dc:date>
    <link>https://github.com/karpathy/char-rnn</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This code implements multi-layer Recurrent Neural Network (RNN, LSTM, and GRU) for training/sampling from character-level language models. The model learns to predict the probability of the next character in a sequence. In other words, the input is a single text file and the model learns to generate text like it.]]></description>
<dc:subject>machinelearning deeplearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:99de8ba56fea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:deeplearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/">
    <title>Top 10 data mining algorithms in plain English | rayli.net</title>
    <dc:date>2015-05-26T13:33:45+00:00</dc:date>
    <link>http://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[I’m going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper.

Once you know what they are, how they work, what they do and where you can find them, my hope is you’ll have this blog post as a springboard to learn even more about data mining.

What are we waiting for? Let’s get started!


Contents [hide]
1. C4.5
2. k-means
3. Support vector machines
4. Apriori
5. EM
6. PageRank
7. AdaBoost
8. kNN
9. Naive Bayes
10. CART]]></description>
<dc:subject>machinelearning algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:a74d12491dcd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/jakevdp/sklearn_pycon2015/">
    <title>jakevdp/sklearn_pycon2015</title>
    <dc:date>2015-04-15T23:14:56+00:00</dc:date>
    <link>https://github.com/jakevdp/sklearn_pycon2015/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[an introduction to the core concepts of machine learning and the Scikit-Learn package. We will introduce the scikit-learn API, and use it to explore the basic categories of machine learning problems and related topics such as feature selection and model validation, and practice applying these tools to real-world data sets.]]></description>
<dc:subject>python machinelearning tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:e350100eba74/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/haifengl/smile">
    <title>haifengl/smile</title>
    <dc:date>2015-03-04T12:46:58+00:00</dc:date>
    <link>https://github.com/haifengl/smile</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[SmileMiner (Statistical Machine Intelligence and Learning Engine) is a pure Java library of various state-of-art machine learning algorithms. SmileMiner is self contained and requires only Java standard library.]]></description>
<dc:subject>java machinelearning statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:00e8a8259184/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:java"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/andersbll/deeppy">
    <title>andersbll/deeppy</title>
    <dc:date>2014-12-20T16:58:11+00:00</dc:date>
    <link>https://github.com/andersbll/deeppy</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[DeepPy tries to combine state-of-the-art deep learning models with a Pythonic interface in an extensible framework.]]></description>
<dc:subject>python deeplearning machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:5f6758a42906/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/minerva-developers/minerva">
    <title>minerva-developers/minerva</title>
    <dc:date>2014-12-18T13:07:54+00:00</dc:date>
    <link>https://github.com/minerva-developers/minerva</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Minerva: a fast and flexible tool for deep learning. It provides ndarray programming interface, just like Numpy. Python bindings and C++ bindings are both available. The resulting code can be run on CPU or GPU. Multi-GPU support is very easy.]]></description>
<dc:subject>deeplearning python machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:f57756425ce8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://prezi.com/jr61rhjoqotb/high-accuracy-metadata/">
    <title>High Accuracy Metadata by Ashleigh Faith on Prezi</title>
    <dc:date>2013-11-26T13:31:01+00:00</dc:date>
    <link>http://prezi.com/jr61rhjoqotb/high-accuracy-metadata/</link>
    <dc:creator>rybesh</dc:creator><dc:subject>inls520 taxonomy automation machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:36c5b9491d00/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls520"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:taxonomy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:automation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.quora.com/What-are-the-advantages-of-different-classification-algorithms#">
    <title>What are the advantages of different classification algorithms? - Quora</title>
    <dc:date>2013-11-14T13:03:17+00:00</dc:date>
    <link>http://www.quora.com/What-are-the-advantages-of-different-classification-algorithms#</link>
    <dc:creator>rybesh</dc:creator><dc:subject>classification algorithms machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:2fbae331b365/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cis.temple.edu/~yates/papers/2013-comp-ling-rep-learning-preprint.pdf">
    <title>Learning Representations for Weakly Supervised Natural Language Processing Tasks</title>
    <dc:date>2013-04-24T18:51:07+00:00</dc:date>
    <link>http://www.cis.temple.edu/~yates/papers/2013-comp-ling-rep-learning-preprint.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Finding the right representations for words is critical for building accurate NLP systems when domain-speciﬁc labeled data for the task is scarce. This paper investigates novel techniques for extracting features from n-gram models, Hidden Markov Models, and other statistical language models, including a novel Partial Lattice Markov Random Field model. Experiments on partof-speech tagging and information extraction, among other tasks, indicate that features taken from statistical language models, in combination with more traditional features, outperform traditional representations alone, and that graphical model representations outperform n-gram models, especially on sparse and polysemous words.]]></description>
<dc:subject>nlp representation machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:290ca6962b9f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.ics.uci.edu/~welling/teaching/ICS273Afall11/IntroMLBook.pdf">
    <title>A First Encounter with Machine Learning</title>
    <dc:date>2013-04-16T01:31:50+00:00</dc:date>
    <link>https://www.ics.uci.edu/~welling/teaching/ICS273Afall11/IntroMLBook.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A simple, intuitive introduction into the concepts of machine learning. A ﬁrst read to whet the appetite so to speak, a prelude to the more technical and advanced textbooks. Meant for those starting out in the ﬁeld who need a simple, intuitive explanation of some of the most useful algorithms that machine learning has to offer.]]></description>
<dc:subject>machinelearning textbook</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:485a639ca783/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textbook"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://guidetodatamining.com/">
    <title>A Programmer's Guide to Data Mining | The Ancient Art of the Numerati</title>
    <dc:date>2013-04-06T15:13:53+00:00</dc:date>
    <link>http://guidetodatamining.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Before you is a tool for learning basic data mining techniques. Most data mining textbooks focus on providing a theoretical foundation for data mining, and as result, may seem notoriously difficult to understand. Don’t get me wrong, the information in those books is extremely important. However, if you are a programmer interested in learning a bit about data mining you might be interested in a beginner’s hands-on guide as a first step. That’s what this book provides.]]></description>
<dc:subject>datamining machinelearning python book</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:0ec40ed11e7e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datamining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:book"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.exp-platform.com/Documents/puzzlingOutcomesInControlledExperiments.pdf">
    <title>Trustworthy Online Controlled Experiments: Five Puzzling Outcomes Explained</title>
    <dc:date>2013-02-13T17:56:56+00:00</dc:date>
    <link>http://www.exp-platform.com/Documents/puzzlingOutcomesInControlledExperiments.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Online controlled experiments are often utilized to make data-driven decisions at Amazon, Microsoft, eBay, Facebook, Google, Yahoo, Zynga, and at many other companies.  While the theory of a controlled experiment is simple, and dates back to Sir Ronald A. Fisher’s experiments at the Rothamsted Agricultural Experimental Station in England in the 1920s, the deployment and mining of online controlled experiments at scale—thousands of experiments now—has taught us many lessons.  These exemplify the proverb that the difference between theory and practice is greater in practice than in theory. We present our learnings as they happened: puzzling outcomes of controlled experiments that we analyzed deeply to understand and explain.  Each of these took multiple-person weeks to months to properly analyze and get to the often surprising root cause. The root causes behind these puzzling results are not isolated incidents; these issues generalized to multiple experiments. The heightened awareness should help readers increase the trustworthiness of the results coming out of controlled experiments.   At Microsoft’s Bing, it is not uncommon to see experiments that impact annual revenue by millions of dollars, thus getting trustworthy results is critical and investing in understanding anomalies has tremendous payoff: reversing a single incorrect decision based on the results of an experiment can fund a whole team of analysts.   The topics we cover include: the OEC (Overall Evaluation Criterion), click tracking, effect trends, experiment length and power, and carryover effects.]]></description>
<dc:subject>statistics machinelearning data analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:d1db8655ddce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://infolab.stanford.edu/~ullman/mmds.html#latest">
    <title>Mining of Massive Datasets</title>
    <dc:date>2013-01-17T04:02:30+00:00</dc:date>
    <link>http://infolab.stanford.edu/~ullman/mmds.html#latest</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[At the highest level of description, this book is about data mining. However, it focuses on data mining of very large amounts of data, that is, data so large it does not ﬁt in main memory. Because of the emphasis on size, many of our examples are about the Web or data derived from the Web. Further, the book takes an algorithmic point of view: data mining is about applying algorithms to data, rather than using data to “train” a machine-learning engine of some sort.]]></description>
<dc:subject>datamining machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:b760fb079f40/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datamining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.inference.phy.cam.ac.uk/mackay/itila/">
    <title>David MacKay: Information Theory, Inference, and Learning Algorithms: Home</title>
    <dc:date>2013-01-17T04:01:12+00:00</dc:date>
    <link>http://www.inference.phy.cam.ac.uk/mackay/itila/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.]]></description>
<dc:subject>machinelearning textbook</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:f110a69df93d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textbook"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.shogun-toolbox.org/page/about/information">
    <title>The SHOGUN Machine Learning Toolbox</title>
    <dc:date>2013-01-05T21:41:06+00:00</dc:date>
    <link>http://www.shogun-toolbox.org/page/about/information</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM) [1]. It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art OCAS [21], Liblinear [20], LibSVM [2], SVMLight, [3] SVMLin [4] and GPDT [5]. Each of the SVMs can be combined with a variety of kernels.]]></description>
<dc:subject>machinelearning tools svm</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:6b2ceef70cb8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:svm"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://bickson.blogspot.co.il/2012/01/vowal-wabbit-tutorial.html">
    <title>Large Scale Machine Learning and Other Animals: Vowpal Wabbit Tutorial</title>
    <dc:date>2013-01-05T21:36:52+00:00</dc:date>
    <link>http://bickson.blogspot.co.il/2012/01/vowal-wabbit-tutorial.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Vowpal Wabbit is a popular online machine learning implementation for solving linear models like LASSO, sparse logistic regression, etc. Library was initiated in and written by John Langford, Yahoo! Research.

Here are some quick instructions on how to install and try it out.]]></description>
<dc:subject>machinelearning tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:6246268d84b2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.mlsurveys.com/">
    <title>Machine Learning Surveys</title>
    <dc:date>2013-01-05T21:31:27+00:00</dc:date>
    <link>http://www.mlsurveys.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A list of literature surveys, reviews, and tutorials on Machine Learning and related topics.]]></description>
<dc:subject>machinelearning research tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:a0a2e6f171cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial">
    <title>Deep Learning Tutorial - www.socher.org</title>
    <dc:date>2012-12-22T02:37:43+00:00</dc:date>
    <link>http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Machine learning is everywhere in today's NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others. The most attractive quality of these techniques is that they can perform well without any external hand-designed resources or time-intensive feature engineering. Despite these advantages, many researchers in NLP are not familiar with these methods. Our focus is on insight and understanding, using graphical illustrations and simple, intuitive derivations. The goal of the tutorial is to make the inner workings of these techniques transparent, intuitive and their results interpretable, rather than black boxes labeled "magic here". The first part of the tutorial presents the basics of neural networks, neural word vectors, several simple models based on local windows and the math and algorithms of training via backpropagation. In this section applications include language modeling and POS tagging. In the second section we present recursive neural networks which can learn structured tree outputs as well as vector representations for phrases and sentences. We cover both equations as well as applications. We show how training can be achieved by a modified version of the backpropagation algorithm introduced before. These modifications allow the algorithm to work on tree structures. Applications include sentiment analysis and paraphrase detection. We also draw connections to recent work in semantic compositionality in vector spaces. The principle goal, again, is to make these methods appear intuitive and interpretable rather than mathematically confusing. By this point in the tutorial, the audience members should have a clear understanding of how to build a deep learning system for word-, sentence- and document-level tasks. The last part of the tutorial gives a general overview of the different applications of deep learning in NLP, including bag of words models. We will provide a discussion of NLP-oriented issues in modeling, interpretation, representational power, and optimization.]]></description>
<dc:subject>machinelearning nlp tutorial deeplearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:6c28a672bea5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:deeplearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://deeplearning.net/software/theano/">
    <title>Welcome — Theano v0.6rc2 documentation</title>
    <dc:date>2012-12-21T18:51:32+00:00</dc:date>
    <link>http://deeplearning.net/software/theano/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.]]></description>
<dc:subject>machinelearning math python deeplearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:5a5079e4d607/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:math"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:deeplearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stanford.edu/~hastie/local.ftp/Springer/ESLII_print5.pdf">
    <title>The Elements of Statistical Learning</title>
    <dc:date>2012-12-07T22:41:40+00:00</dc:date>
    <link>http://www.stanford.edu/~hastie/local.ftp/Springer/ESLII_print5.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This book is our attempt to bring together many of the important new ideas in machine learning, and explain them in a statistical framework. While some mathematical details are needed, we emphasize the methods and their conceptual underpinnings rather than their theoretical properties. As a result, we hope that this book will appeal not just to statisticians but also to researchers and practitioners in a wide variety of ﬁelds.]]></description>
<dc:subject>statistics machinelearning textbook</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:396d31117452/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textbook"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://aclweb.org/anthology-new/P/P12/P12-1078.pdf">
    <title>ACL 2012/Historical Analysis of Legal Opinions with a Sparse Mixed-Effects Latent Variable Model</title>
    <dc:date>2012-10-06T23:51:16+00:00</dc:date>
    <link>http://aclweb.org/anthology-new/P/P12/P12-1078.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[We show that the joint learning scheme of our sparse mixed-effects model improves on other state-of-the-art generative and discriminative models on the region and time period identification tasks.]]></description>
<dc:subject>textanalysis law periodization place machinelearning history opinion datamining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:6c3b2a080cb1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textanalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:law"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:periodization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:place"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:opinion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datamining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://aclweb.org/anthology-new/E/E12/E12-1051.pdf">
    <title>Instance-Driven Attachment of Semantic Annotations over Conceptual Hierarchies</title>
    <dc:date>2012-10-06T23:45:29+00:00</dc:date>
    <link>http://aclweb.org/anthology-new/E/E12/E12-1051.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Whether automatically extracted or human generated, open-domain factual knowledge is often available in the form of semantic annotations (e.g., composed-by ) that take one or more specific instances (e.g., rhapsody in blue , george gershwin ) as their arguments. This paper introduces a method for converting flat sets of instance-level annotations into hierarchically organized, concept-level annotations, which capture not only the broad semantics of the desired arguments (e.g., `People' rather than `Locations'), but also the correct level of generality (e.g., `Composers' rather than `People', or `Jazz Composers'). The method refrains from encoding features specific to a particular domain or annotation, to ensure immediate applicability to new, previously unseen annotations. Over a gold standard of semantic annotations and concepts that best capture their arguments, the method substantially outperforms three baselines, on average, computing concepts that are less than one step in the hierarchy away from the corresponding gold standard concepts.]]></description>
<dc:subject>concepts taxonomy semantics machinelearning inls520</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:9d31bb3a5d2c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:concepts"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:taxonomy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semantics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls520"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mcmc-jags.sourceforge.net/">
    <title>JAGS - Just Another Gibbs Sampler</title>
    <dc:date>2012-10-03T22:14:44+00:00</dc:date>
    <link>http://mcmc-jags.sourceforge.net/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[JAGS is Just Another Gibbs Sampler.  It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation  not wholly unlike BUGS.]]></description>
<dc:subject>machinelearning bayesian data analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:f2dca9013d7b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:bayesian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.behind-the-enemy-lines.com/2008/03/mechanical-turk-foundations.html">
    <title>Mechanical Turk: The Foundations | A Computer Scientist in a Business School</title>
    <dc:date>2012-10-03T15:12:31+00:00</dc:date>
    <link>http://www.behind-the-enemy-lines.com/2008/03/mechanical-turk-foundations.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Over the last year or so, I have been using Mechanical Turk as a very useful tool for my research. While in practice it is a very useful tool, there is high uncertainty about the quality of the answers that someone can get back from such a system. Some of the Turkers will be lazy and submit random answers, some will have good intentions but still submit an incorrect answer, and some others will do a good job. However, since we do not know beforehand the actual answers for the questions, we need methods for extracting the signal from the noise, evaluate the quality of the individual Turkers, and to decide how much effort to spend annotating our data.]]></description>
<dc:subject>crowdsourcing annotation nlp machinelearning statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:7cc0f51d4dc3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:crowdsourcing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:annotation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lingpipe.files.wordpress.com/2008/04/ed-2010-slides.pdf">
    <title>Whence Linguistic Data?</title>
    <dc:date>2012-10-03T15:11:42+00:00</dc:date>
    <link>http://lingpipe.files.wordpress.com/2008/04/ed-2010-slides.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Statistical techniques for inferring gold standards from noisy Mechanical Turk annotations.]]></description>
<dc:subject>crowdsourcing nlp annotation machinelearning statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:4eda55c84201/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:crowdsourcing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:annotation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arkitus.com/PRML/">
    <title>S. M. Ali Eslami / Patterns for Research in Machine Learning</title>
    <dc:date>2012-10-01T00:15:17+00:00</dc:date>
    <link>http://arkitus.com/PRML/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This page lists a handful of code patterns that I wish I was more aware of when I started my PhD. Each on its own may seem pointless, but collectively they go a long way towards making the typical research workflow more efficient. And an efficient workflow makes it just that little bit easier to ask the research questions that matter.

My guess is that these patterns will not only be useful for machine learning, but also any other computational work that involves either a) processing large amounts of data, or b) algorithms that take a significant amount of time to execute.]]></description>
<dc:subject>machinelearning research advice patterns</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:21f108f04889/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:advice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:patterns"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/JohnLangford/vowpal_wabbit/wiki">
    <title>Home · JohnLangford/vowpal_wabbit Wiki</title>
    <dc:date>2012-10-01T00:13:54+00:00</dc:date>
    <link>https://github.com/JohnLangford/vowpal_wabbit/wiki</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[There are two ways to have a fast learning algorithm: (a) start with a slow algorithm and speed it up, or (b) build an intrinsically fast learning algorithm. This project is about approach (b), and it's reached a state where it may be useful to others as a platform for research and experimentation.]]></description>
<dc:subject>datamining machinelearning tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:530755f889ed/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datamining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://aliquote.org/memos/2012/09/20/interacting-with-weka-from-jython">
    <title>www.aliquote.org: Interacting with Weka from Jython</title>
    <dc:date>2012-09-22T04:15:17+00:00</dc:date>
    <link>http://aliquote.org/memos/2012/09/20/interacting-with-weka-from-jython</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[You can use WEKA directly with Jython in a friendly interactive REPL.]]></description>
<dc:subject>machinelearning python interface</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:566d6d9722a3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:interface"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://icml.cc/2012/papers/298.pdf">
    <title>Machine Learning that Matters</title>
    <dc:date>2012-07-25T20:24:50+00:00</dc:date>
    <link>http://icml.cc/2012/papers/298.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. From this perspective, there exist glaring limitations in the data sets we investigate, the metrics we employ for evaluation, and the degree to which results are communicated back to their originating domains.  What changes are needed to how we conduct research to increase the impact that ML has? We present six Impact Challenges to explicitly focus the ﬁeld’s energy and attention, and we discuss existing obstacles that must be addressed. We aim to inspire ongoing discussion and focus on ML that matters.]]></description>
<dc:subject>machinelearning society evaluation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:d27caf01f279/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:society"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:evaluation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/270212.pdf">
    <title>Bayesian Reasoning and Machine Learning</title>
    <dc:date>2012-07-25T02:49:13+00:00</dc:date>
    <link>http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/270212.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In the early stages of Machine Learning and related areas, similar techniques were discovered in relatively isolated research communities. This book presents a unified treatment via graphical models, a marriage between graph and probability theory, facilitating the transference of Machine Learning concepts between different branches of the mathematical and computational sciences.]]></description>
<dc:subject>machinelearning statistics reference textbook</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:58030a9e1054/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:reference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textbook"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.journalism.org/commentary_backgrounder/About+Campaign+2012+in+the+Media+">
    <title>About Campaign 2012 in the Media | Project for Excellence in Journalism (PEJ)</title>
    <dc:date>2012-06-01T12:03:06+00:00</dc:date>
    <link>http://www.journalism.org/commentary_backgrounder/About+Campaign+2012+in+the+Media+</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[To arrive at the results regarding the tone of coverage, PEJ employed computer coding software developed by Crimson Hexagon along with PEJ's traditional media research methods.

The technology for Crimson Hexagon is rooted in an algorithm created by Gary King, a professor at Harvard University's Institute for Quantitative Social Science. (Click here to view the study explaining the algorithm.)

According to Crimson Hexagon, the purpose of computer coding is to "take as data a potentially large set of text documents, of which a small subset is hand coded into an investigator-chosen set of mutually exclusive and exhaustive categories. As output, the methods give approximately unbiased and statistically consistent estimates of the proportion of all documents in each category."]]></description>
<dc:subject>news textanalysis sentiment machinelearning classification</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:81e5928599f7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:news"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textanalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:sentiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:classification"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1203.6402">
    <title>[1203.6402] Scalable K-Means++</title>
    <dc:date>2012-03-30T12:51:53+00:00</dc:date>
    <link>http://arxiv.org/abs/1203.6402</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Over half a century old and showing no signs of aging, k-means remains one of the most popular data processing algorithms. As is well-known, a proper initialization of k-means is crucial for obtaining a good final solution. The recently proposed k-means++ initialization algorithm achieves this, obtaining an initial set of centers that is provably close to the optimum solution. A major downside of the k-means++ is its inherent sequential nature, which limits its applicability to massive data: one must make k passes over the data to find a good initial set of centers. In this work we show how to drastically reduce the number of passes needed to obtain, in parallel, a good initialization. This is unlike prevailing efforts on parallelizing k-means that have mostly focused on the post-initialization phases of k-means. We prove that our proposed initialization algorithm k-means|| obtains a nearly optimal solution after a logarithmic number of passes, and then show that in practice a constant number of passes suffices. Experimental evaluation on real-world large-scale data demonstrates that k-means|| outperforms k-means++ in both sequential and parallel settings.]]></description>
<dc:subject>clustering machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:7f7c9a8f68dd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:clustering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jmlr.csail.mit.edu/proceedings/papers/v22/hoai12/hoai12.pdf">
    <title>Maximum Margin Temporal Clustering</title>
    <dc:date>2012-03-26T22:25:06+00:00</dc:date>
    <link>http://jmlr.csail.mit.edu/proceedings/papers/v22/hoai12/hoai12.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Temporal Clustering (TC) refers to the factorization of multiple time series into a set of non-overlapping segments that belong to k temporal clusters. Existing methods based on extensions of generative models such as k -means or Switching Linear Dynamical Systems (SLDS) often lead to intractable inference and lack a mechanism for feature selection, critical when dealing with high dimensional data. To overcome these limitations, this paper proposes Maximum Margin Temporal Clustering (MMTC). MMTC simultaneously determines the start and the end of each segment, while learning a multi-class Support Vector Machine (SVM) to discriminate among temporal clusters. MMTC extends Maximum Margin Clustering in two ways: first, it incorporates the notion of TC, and second, it introduces additional constraints to achieve better balance between clusters. Experiments on clustering human actions and bee dancing motions illustrate the benefits of our approach compared to state-of-the-art methods.]]></description>
<dc:subject>temporality actions events clustering supervised machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:ce95da4ebd75/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:temporality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:actions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:events"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:clustering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:supervised"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://gking.harvard.edu/data?dvn_subpage=/faces/study/StudyPage.xhtml?globalId=hdl:1902.1/FYXLAWZRIA">
    <title>10 MILLION INTERNATIONAL DYADIC EVENTS</title>
    <dc:date>2012-03-21T22:47:23+00:00</dc:date>
    <link>http://gking.harvard.edu/data?dvn_subpage=/faces/study/StudyPage.xhtml?globalId=hdl:1902.1/FYXLAWZRIA</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[When the Palestinians launch a mortar attack into Israel, the Israeli army does not wait until the end of the calendar year to react. Yet, most modern data collections are aggregated to the month or year. The data available here include almost 10 million individual events, each coded to the exact day they occur or become known. Each event is summarized in the data as "Actor A does something to Actor B", with Actors A and B recording about 450 countries and other (within-country) actors and "does something to" coded in an ontology of about 200 types of actions. The data are coded by computer from millions of Reuters news reports. The software system (produced by VRA) that performs this task has been independently evaluated by King and Lowe (2003). This article found that for the numbers of events it was possible to convince humans (trained Harvard undergraduates) to code by hand, the machine did as well as the humans. For much larger numbers of events for which no expert coder could keep up, the machine dominates.]]></description>
<dc:subject>events politicalscience data machinelearning textanalysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:ea4e26b4c1c8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:events"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:politicalscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textanalysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.princeton.edu/~blei/papers/Blei2011.pdf">
    <title>Blei - Introduction to Probabilistic Topic Models</title>
    <dc:date>2012-03-19T19:09:41+00:00</dc:date>
    <link>http://www.cs.princeton.edu/~blei/papers/Blei2011.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Probabilistic topic models are a suite of algorithms whose aim is to discover the hidden thematic structure in large archives of documents. In this article, we review the main ideas of this field, survey the current state-of-the-art, and describe some promising future directions. We first describe latent Dirichlet allocation (LDA) [8], which is the simplest kind of topic model. We discuss its connections to probabilistic modeling, and describe two kinds of algorithms for topic discovery. We then survey the growing body of research that extends and applies topic models in interesting ways. These extensions have been developed by relaxing some of the statistical assumptions of LDA, incorporating meta-data into the analysis of the documents, and using similar kinds of models on a diversity of data types such as social networks, images and genetics.  Finally, we give our thoughts as to some of the important unexplored directions for topic modeling. These include rigorous methods for checking models built for data exploration, new approaches to visualizing text and other high dimensional data, and moving beyond traditional information engineering applications towards using topic models for more scientific ends.]]></description>
<dc:subject>topicmodels unsupervised machinelearning clustering</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:10fbdf70aca0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:topicmodels"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:unsupervised"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:clustering"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://chasen.org/~taku/software/TinySVM/">
    <title>TinySVM: Support Vector Machines</title>
    <dc:date>2012-03-09T02:54:58+00:00</dc:date>
    <link>http://chasen.org/~taku/software/TinySVM/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[TinySVM is an implementation of Support Vector Machines (SVMs) [Vapnik 95], [Vapnik 98] for the problem of pattern recognition.]]></description>
<dc:subject>svm machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:4d9e0d330160/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:svm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.svms.org/software.html">
    <title>Support Vector Machines: Software</title>
    <dc:date>2012-03-09T02:37:40+00:00</dc:date>
    <link>http://www.svms.org/software.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Nice ranked list of SVM software.]]></description>
<dc:subject>svm machinelearning classification</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:aac6068f4ab7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:svm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:classification"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.echen.me/2012/01/03/introduction-to-conditional-random-fields/">
    <title>Introduction to Conditional Random Fields - Edwin Chen's Blog</title>
    <dc:date>2012-03-06T20:17:01+00:00</dc:date>
    <link>http://blog.echen.me/2012/01/03/introduction-to-conditional-random-fields/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Accessible introduction to CRFs.]]></description>
<dc:subject>crf machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:9c6a3ac44dba/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:crf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1003.0783">
    <title>[1003.0783] Supervised Topic Models</title>
    <dc:date>2012-03-06T19:58:14+00:00</dc:date>
    <link>http://arxiv.org/abs/1003.0783</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents. The model accommodates a variety of response types. We derive an approximate maximum-likelihood procedure for parameter estimation, which relies on variational methods to handle intractable posterior expectations. Prediction problems motivate this research: we use the fitted model to predict response values for new documents. We test sLDA on two real-world problems: movie ratings predicted from reviews, and the political tone of amendments in the U.S. Senate based on the amendment text. We illustrate the benefits of sLDA versus modern regularized regression, as well as versus an unsupervised LDA analysis followed by a separate regression.]]></description>
<dc:subject>slda classification lda topicmodels textanalysis machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:9c900b5fec08/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:slda"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:lda"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:topicmodels"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textanalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.princeton.edu/~chongw/slda/">
    <title>Supervised latent Dirichlet allocation for classification</title>
    <dc:date>2012-03-06T19:57:23+00:00</dc:date>
    <link>http://www.cs.princeton.edu/~chongw/slda/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This is a C++ implementation of supervised latent Dirichlet allocation (sLDA) for classification.]]></description>
<dc:subject>c++ slda classification topicmodels lda machinelearning textanalysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:83cdb66b3f94/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:c++"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:slda"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:topicmodels"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:lda"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textanalysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.princeton.edu/~blei/lda-c/">
    <title>Latent Dirichlet Allocation in C</title>
    <dc:date>2012-03-06T19:49:03+00:00</dc:date>
    <link>http://www.cs.princeton.edu/~blei/lda-c/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This is a C implementation of variational EM for latent Dirichlet allocation (LDA), a topic model for text or other discrete data. LDA allows you to analyze of corpus, and extract the topics that combined to form its documents. For example, click here to see the topics estimated from a small corpus of Associated Press documents. LDA is fully described in Blei et al. (2003) .

This code contains:

an implementation of variational inference for the per-document topic proportions and per-word topic assignments
a variational EM procedure for estimating the topics and exchangeable Dirichlet hyperparameter]]></description>
<dc:subject>lda c linguistics machinelearning textanalysis textmining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:2469cf74384a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:lda"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:c"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:linguistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textanalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textmining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www-stat.stanford.edu/~tibs/ElemStatLearn/">
    <title>Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.</title>
    <dc:date>2012-03-06T18:52:12+00:00</dc:date>
    <link>http://www-stat.stanford.edu/~tibs/ElemStatLearn/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[During the past decade has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book descibes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book. 

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization and spectral clustering. There is also a chapter on methods for ``wide'' data (italics p bigger than n), including multiple testing and false discovery rates. 

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful {italics An Introduct ion to the Bootstrap}. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.]]></description>
<dc:subject>statistics machinelearning datamining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:d18e8214fa85/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datamining"/>
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</item>
<item rdf:about="https://bitbucket.org/wcauchois/pysvmlight/">
    <title>wcauchois / pysvmlight / overview — Bitbucket</title>
    <dc:date>2012-03-04T20:28:11+00:00</dc:date>
    <link>https://bitbucket.org/wcauchois/pysvmlight/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A Python binding to the popular "SVM-Light" support vector machine library.]]></description>
<dc:subject>svm machinelearning python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:b7e537d51187/</dc:identifier>
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</item>
<item rdf:about="http://www.inference.phy.cam.ac.uk/hmw26/crf/">
    <title>Conditional Random Fields</title>
    <dc:date>2012-02-03T15:29:07+00:00</dc:date>
    <link>http://www.inference.phy.cam.ac.uk/hmw26/crf/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Conditional random fields (CRFs) are a probabilistic framework for labeling and segmenting structured data, such as sequences, trees and lattices. The underlying idea is that of defining a conditional probability distribution over label sequences given a particular observation sequence, rather than a joint distribution over both label and observation sequences. The primary advantage of CRFs over hidden Markov models is their conditional nature, resulting in the relaxation of the independence assumptions required by HMMs in order to ensure tractable inference. Additionally, CRFs avoid the label bias problem, a weakness exhibited by maximum entropy Markov models (MEMMs) and other conditional Markov models based on directed graphical models. CRFs outperform both MEMMs and HMMs on a number of real-world tasks in many fields, including bioinformatics, computational linguistics and speech recognition.]]></description>
<dc:subject>machinelearning nlp crf textmining metadata</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:25e87edcbc6e/</dc:identifier>
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<item rdf:about="http://works.bepress.com/cgi/viewcontent.cgi?article=1026&amp;context=mireille_hildebrandt">
    <title>The Meaning and The Mining of Legal Texts</title>
    <dc:date>2012-01-08T23:37:36+00:00</dc:date>
    <link>http://works.bepress.com/cgi/viewcontent.cgi?article=1026&amp;context=mireille_hildebrandt</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Positive law, inscribed in legal texts, entails an authority not inherent in literary texts, generating legal consequences that can have real effects on a person’s life and liberty. The interpretation of legal texts, necessarily a normative undertaking, resists the mechanical application of rules, though still requiring a measure of predictability, coherence with other relevant legal norms and compliance with constitutional safeguards. The present proliferation of legal texts on the internet (codes, statutes, judgments, treaties, doctrinal treatises) renders the selection of relevant texts and cases next to impossible. We may expect that systems to mine these texts to find arguments that support one’s case, as well as expert systems that support the decision-making process of courts, will end up doing much of the work.

This raises the question of the difference between human interpretation and computational pattern-recognition and the issue of whether this difference makes a difference for the meaning of law. Possibly, data mining will produce patterns that disclose habits of the minds of judges and legislators that would have otherwise gone unnoticed (reinforcing the argument of the ‘legal realists’ at the beginning of the 20th century). Also, after the data analysis it will still be up to the judge to decide how to interpret the results or up to the prosecution which patterns to engage in the construction of evidence (requiring a hermeneutics of computational patterns instead of texts). My focus in this paper regards the fact that the mining process necessarily disambiguates the legal texts in order to transform them into a machine-readable data set, while the algorithms used for the analysis embody a strategy that will co-determine the outcome of the patterns. There seems a major due process concern here to the extent that these patterns are invisible for the naked human eye and will not be contestable in a court of law, due to their hidden complexity and computational nature.

This position paper aims to explain what is at stake in the computational turn with regard to legal texts. This prepares for the question I want to put forward to those involved in distant reading and not-reading of texts: could a visualization of computational patterns constitute a new way of un-hiding the complexity involved, opening the results of computational ‘knowledge’ to citizens’ scrutiny?]]></description>
<dc:subject>textmining machinelearning visualization digitalhumanities law</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:0f19fa010aaa/</dc:identifier>
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