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Recent Posts – Vincent Dumoulin

Your models in Pylearn2. October 10, 2014. A tutorial on the minimal effort required to develop a new model in Pylearn2. Introducing the VAE framework in Pylearn2. October 08, 2014. Combining variational autoencoders with 'Not your grandfather's machine learning library'. August 28, 2014. Some cool demo I made using a VAE. April 30, 2014. Some more RNN attempts. March 28, 2014. A first attempt at training RNNs. Iterating over variable-length sequences. March 19, 2014. RNNs using the TIMIT dataset. Introd...

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Recent Posts – Vincent Dumoulin | vdumoulin.github.io Reviews

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Your models in Pylearn2. October 10, 2014. A tutorial on the minimal effort required to develop a new model in Pylearn2. Introducing the VAE framework in Pylearn2. October 08, 2014. Combining variational autoencoders with 'Not your grandfather's machine learning library'. August 28, 2014. Some cool demo I made using a VAE. April 30, 2014. Some more RNN attempts. March 28, 2014. A first attempt at training RNNs. Iterating over variable-length sequences. March 19, 2014. RNNs using the TIMIT dataset. Introd...

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Recent Posts – Vincent Dumoulin | vdumoulin.github.io Reviews

https://vdumoulin.github.io

Your models in Pylearn2. October 10, 2014. A tutorial on the minimal effort required to develop a new model in Pylearn2. Introducing the VAE framework in Pylearn2. October 08, 2014. Combining variational autoencoders with 'Not your grandfather's machine learning library'. August 28, 2014. Some cool demo I made using a VAE. April 30, 2014. Some more RNN attempts. March 28, 2014. A first attempt at training RNNs. Iterating over variable-length sequences. March 19, 2014. RNNs using the TIMIT dataset. Introd...

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(Yet) another update on the state of TIMIT dataset in Pylearn2 – Vincent Dumoulin

http://vdumoulin.github.io/articles/timit-part-4

Yet) another update on the state of TIMIT dataset in Pylearn2. Feb 19, 2014. A (pretty pretty much) finished business (will this ever end? Remember my last post talking about improvements to the TIMIT dataset? Well here’s another big improvement: thanks to Laurent’s and David’s help, I was able to. Reduce memory footprint, which was the main thing on my to-do list for this week. The problem is that the number of possible training examples is huge: the validation set by itself roughly contains 24. The sol...

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NADEs: an introduction – Vincent Dumoulin

http://vdumoulin.github.io/articles/nade

Feb 25, 2014. A summary of my understanding of NADEs. I might use neural autoregressive distribution estimators (NADEs) for the speech synthesis project; this has to do with an idea both Guillaume Desjardins and Yoshua Bengio talked about in the past couple days, and which I’ll detail later on. For now, I’d like to test my understanding of NADEs by introducing them in a blog post. As they say,. NADE ( original paper. Is a model proposed by Hugo Larochelle. As you can see both in the graph and in the join...

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About Me – Vincent Dumoulin

http://vdumoulin.github.io/about

I am a Ph.D. candidate at MILA. In Université de Montréal. I am co-advised by Yoshua Bengio. I am interested in generative modelling applied to computer vision. Your models in Pylearn2. Oct 10, 2014. Introducing the VAE framewo. Oct 08, 2014. Aug 28, 2014. 2016 Vincent Dumoulin. Powered by Jekyll.

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First Post – Vincent Dumoulin

http://vdumoulin.github.io/articles/first-post

Jan 09, 2014. My very first post. Just a rubbish post used to test code highlighting features. This function builds a YAML string from. Taking the values of hyper-parameters from. Creates the corresponding object and trains it (like train.py), then run the function in. On it, and store the returned values into. Convert nested DD into nested ydict. This will be the complete yaml string that should be executed. Instantiate an object from YAML string. And parameters to contain only one single model.'.

5

Introducing the VAE framework in Pylearn2 – Vincent Dumoulin

http://vdumoulin.github.io/articles/introducing-vae

Introducing the VAE framework in Pylearn2. Oct 08, 2014. Combining variational autoencoders with 'Not your grandfather's machine learning library'. After quite some time spent on the pull request, I’m proud to announce that the VAE model is now integrated in Pylearn2. In this post, I’ll go over the main features of the VAE framework and how to extend it. I will assume the reader is familiar with the VAE model. If not, have a look at my VAE demo webpage. As well as the (Kingma and Welling). In order to ba...

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Class Project Description – IFT6266 – H2015 Representation Learning

https://ift6266h15.wordpress.com/class-project

IFT6266 – H2015 Representation Learning. A mostly deep learning course. Project Blogs and Repos. An important part of the learning experience associated with this course (and 40% of the grade) comes from experimenting with the algorithms presented in class. This page describes what is expected from the students. Feel free to ask questions below. Get hands-on experience with some of the algorithms presented in the course. Practice the collaborative competition typically enjoyed by scientists:. Each studen...

ift6266h14.wordpress.com

Experimenting | Representation Learning - ift6266h14

https://ift6266h14.wordpress.com/experimenting

Representation Learning – ift6266h14. Yoshua Bengio's graduate class on representation learning and deep learning. An important part of the learning experience associated with this course (and 40% of the grade) comes from experimenting with the algorithms presented in class. This page describes what is expected from the students. Feel free to ask questions below. The objectives are the following. Get hands-on experience with some of the algorithms presented in the course. Each student competes to obtain ...

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Dictionary Learning and Sparse Coding for Speech Signals | seaandsailor

http://www.seaandsailor.com/dictlearning.html

Dictionary Learning and Sparse Coding for Speech Signals. Feb 25, 2014. Sparse signal approximations are the basis for a variety of signal processing techniques. Such approximations are usually employed with the objective of having a signal representation that is more meaningful, malleable, and robust to noise than the ones obtained by standard transform methods [Sturm2009]. Orthogonal Matching Pursuit (OMP) [Pati1993]. Or basis pursuit [Chen2001]. Sparse approximation problem formulations. Sparsity-cons...

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