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inFERENCeposts on machine learning, statistics, opinions on things I'm reading in the space
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posts on machine learning, statistics, opinions on things I'm reading in the space
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inFERENCe | inference.vc Reviews
https://inference.vc
posts on machine learning, statistics, opinions on things I'm reading in the space
inference.vc
inFERENCe - Page 2
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Posts on machine learning, statistics, opinions on things I'm reading in the space. Are Energy-Based GANs any more energy-based than normal GANs? September 15th, 2016. How powerful are Graph Convolutions? Review of Kipf and Welling, 2016). September 13th, 2016. The Variational Rényi Lower Bound. September 8th, 2016. InfoGAN: using the variational bound on mutual information (twice). August 4th, 2016. Time-Contrastive Learning for Latent Variable Models. July 14th, 2016. June 15th, 2016. May 12th, 2016.
About Ferenc
http://www.inference.vc/about
Posts on machine learning, statistics, opinions on things I'm reading in the space. My name is Ferenc Huszár. I am a machine learning researcher, I did my PhD in Cambridge with Carl Rasmussen, Máté Lengyel and Zoubin Ghahramani. I'm interested in probabilistic inference, generative models, unsupervised learning and applying deep learning to these problems. Twitter Cortex, etc. I work at Twitter Cortex on unsupervised learning for visual data. We are building some awesome products that will make video...
Variational Inference using Implicit Models, Part I: Bayesian Logistic Regression
http://www.inference.vc/variational-inference-with-implicit-probabilistic-models-part-1-2
Posts on machine learning, statistics, opinions on things I'm reading in the space. January 12th, 2017. Variational Inference using Implicit Models, Part I: Bayesian Logistic Regression. This post is part of a series of tutorials on using implicit models for variational inference. Here's a table of contents so far:. You are here): Inference of single, global variable (Bayesian logistic regression). Amortised Inference via the Prior-Contrastive Method (Explaining Away Demo). We can sample from easily, and.
InfoGAN: using the variational bound on mutual information (twice)
http://www.inference.vc/infogan-variational-bound-on-mutual-information-twice
Posts on machine learning, statistics, opinions on things I'm reading in the space. August 4th, 2016. InfoGAN: using the variational bound on mutual information (twice). Many people have recommended me the infoGAN paper, but I hadn't taken the time to read it until recently. It is actually quite cool:. Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel (2016) InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. If we were succe...
How powerful are Graph Convolutions? (review of Kipf & Welling, 2016)
http://www.inference.vc/how-powerful-are-graph-convolutions-review-of-kipf-welling-2016-2
Posts on machine learning, statistics, opinions on things I'm reading in the space. September 13th, 2016. How powerful are Graph Convolutions? Review of Kipf and Welling, 2016). This post is about a paper that has just come out recently on practical generalizations of convolutional layers to graphs:. Thomas N. Kipf and Max Welling (2016) Semi-Supervised Classification with Graph Convolutional Networks. Along the way I found this earlier, related paper:. Which I highly recommend. Summary of this post.
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DANIEL SLATER'S BLOG: Using Net2Net to speed up network training
http://www.danielslater.net/2016/05/using-net2net-to-speed-up-network.html
Thursday, May 26, 2016. Using Net2Net to speed up network training. When training neural networks there are 2 things that combine to make life frustrating:. Neural networks can take an insane amount of time of train. If a network could be trained quickly number 2 wouldn't really matter, we could just do a grid search(or even particle swarm optimization. Or maybe Bayesian optimization. The amount of time to train is counted in days so better hope your first guess was good. I have posted a numpy implementa...
DANIEL SLATER'S BLOG: June 2015
http://www.danielslater.net/2015_06_01_archive.html
Thursday, June 18, 2015. Why there is a big unsupervised learning shaped whole in the universe. Unfortunately so far we are a long way from that and the technique shown here seems trivial compared to that goal. But the goal is interesting enough that it is worth pursuing. The answer to the question "what am I asking an unsupervised network to do? An example competitive learning neural net. It is also available in C#. From random import uniform class UnsupervisedNN(object): def init (self, size of input a...
DANIEL SLATER'S BLOG: October 2015
http://www.danielslater.net/2015_10_01_archive.html
Tuesday, October 27, 2015. Even more quick summaries of research papers. Following on from part 1. Here are even more quick summaries of research papers,. Generative NeuroEvolution for DeepLearning. Uses the MNIST hand-drawn digits dataset, trains both a normal deep network and a convolutional network. HyperNEAT on it's own performs very badly. Using HyperNEAT to generate a number of layers and then backprop on the final layer is achieves 58.4% for normal ANNs. ES-HyperNeat uses the connection weights va...
DANIEL SLATER'S BLOG: August 2015
http://www.danielslater.net/2015_08_01_archive.html
Saturday, August 29, 2015. Presenting WikiDataDotNet - Client API for WikiData. Is one of those things that sets the mind boggling at the possibilities of the internet. It's a project, started by the WikiMedia foundation, to collect structured data on everything. If you are doing anything related to machine learning, it is the best source of data I have so far found. Https:/ www.wikidata.org/w/api.php? This will return a JSON file with sections like:. Here we see the id of the item, in this case Q38.
DANIEL SLATER'S BLOG: Deep-Q learning Pong with Tensorflow and PyGame
http://www.danielslater.net/2016/03/deep-q-learning-pong-with-tensorflow.html
Sunday, March 13, 2016. Deep-Q learning Pong with Tensorflow and PyGame. In a previous post. We went built a framework for running learning agents against PyGame. Now we'll try and build something in it that can learn to play Pong. We will be aided in this quest by two trusty friends Tensorflow. Google's recently released numerical computation library and this paper. On reinforcement learning for Atari games by Deepmind. Is a good starting point for learning. You will need Python 2 or 3 installed. In the...
DANIEL SLATER'S BLOG: May 2016
http://www.danielslater.net/2016_05_01_archive.html
Thursday, May 26, 2016. Using Net2Net to speed up network training. When training neural networks there are 2 things that combine to make life frustrating:. Neural networks can take an insane amount of time of train. If a network could be trained quickly number 2 wouldn't really matter, we could just do a grid search(or even particle swarm optimization. Or maybe Bayesian optimization. The amount of time to train is counted in days so better hope your first guess was good. I have posted a numpy implementa...
DANIEL SLATER'S BLOG: Mini-Pong and Half-Pong
http://www.danielslater.net/2016/05/mini-pong-and-half-pong.html
Monday, May 2, 2016. I'm going to be giving a talk/tutorial at PyDataLondon 2016. On Friday the 6th of may, if your in London that weekend I would recommend going, there are going to be lots of interesting talks, and if you do go please say hi. Possibly they are using other tricks not reported in the paper, or just lots of hyper parameter tuning, or there are still more bugs in my implementation(entirely possible, if anyone finds any please submit). Distance from building our future robot overlords, stil...
Links - FastML
http://fastml.com/links
Machine learning made easy. A couple of pages we occasionaly read:. GitHub meets Arxiv, or machine learning papers with code. Heavy focus on deep learning. An Armenian blog on neural networks. Delip Rao recently started blogging, looks good so far. Https:/ jmetzen.github.io. Jan Hendrik Metzen has a blog on machine learning and python. We posit that you’re gonna like it. Http:/ www.inference.vc. Ferenc Huszar explains some deep learning papers he liked. Https:/ jakevdp.github.io/. Paul Mineiro, at. It an...
DANIEL SLATER'S BLOG: November 2015
http://www.danielslater.net/2015_11_01_archive.html
Sunday, November 8, 2015. Quick summaries of research papers around dynamically generating network structure. I've been reading a lot of research papers on how the structure of ANN can be generated/detected. Here are some quick summaries of interesting papers in that area. Dynamic Node creation in backpropagation networks. Looks at attempting to find a good number of hidden nodes for a network by starting small and growing the correct number of hidden nodes. After training a standard ANN we can potential...
DANIEL SLATER'S BLOG: PyDataLondon 2016
http://www.danielslater.net/2016/05/pydatalondon-2016.html
Sunday, May 15, 2016. Last week I gave a talk at PyDataLondon 2016. Hosted at the Bloomberg offices in central London. If you don't know anything about PyData. It is an community of Python data science enthusiasts that run various meetups and conferences across the world. If your interested in that sort of thing and they are running something near to you I would highly recommend checking it out. Below is the YouTube video for my talk and. Is the associated GitHub. Which includes all the example code.
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Inference Group: Home
Cavendish Laboratory, Cambridge. David MacKay's group works on machine learning and information theory. Current projects involve neural networks, automated Go playing, the design of record-breaking error-correcting codes and quantum error-correcting codes, and the construction of human-computer interfaces that make use of adaptive language models. News - last updated May 2013. Of the Engineering Department. Researchers who would like to work with David MacKay on whole-system energy modelling. Site last m...
inference in a sentence | simple examples
In A Sentence .org. The best little site that helps you understand word usage with examples. Inference in a sentence. I was thinking Strongtalk Tracing Type. Sorry, I meant. This is why we have statistical. The I in RTTI stands for identification, not. Has not been done in python. The article is talking about partial type. And most implementations of type. Ive seen do partial type. Thats a form of static typing (via type. Maybe I should have said they dont have type. It doesnt seem to provide any Bayesian.
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Inference Group: Home
Cavendish Laboratory, Cambridge. David MacKay's group works on machine learning and information theory. Current projects involve neural networks, automated Go playing, the design of record-breaking error-correcting codes and quantum error-correcting codes, and the construction of human-computer interfaces that make use of adaptive language models. News - last updated May 2013. Of the Engineering Department. Researchers who would like to work with David MacKay on whole-system energy modelling. Site last m...
Inference
Rule Engine for .NET. Business Rule Center of Excellence. Health and Human Services. Intelligence and Law Enforcement. White Papers and Datasheets. Reduce your JRules/ODM licensing fees. Migrate your business rules for substantial savings. Write business rules NOT code. Deploy Big Data insights using familiar Microsoft tools: Office and SharePoint. ART's authoring environment allows business users and analysts to rapidly utilise business intelligence. Here's how. Feel right at home from Day 1. Create SOA...
inFERENCe
Posts on machine learning, statistics, opinions on things I'm reading in the space. Variational Inference with Implicit Probabilistic Models: Part 1. January 12th, 2017. 🎄 2016 Holiday Special: Deriving the Subpixel CNN from First Principles. December 23rd, 2016. New Perspectives on Adversarial Training (NIPS 2016 Adversarial Training Workshop). December 14th, 2016. Solving Intelligence vs Hastening the Arrival of the Next AI Winter. November 24th, 2016. October 20th, 2016. September 15th, 2016.
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