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IFT6266 | Representation LearningRepresentation Learning
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Representation Learning
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IFT6266 | Representation Learning | legaultma.wordpress.com Reviews
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Representation Learning
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Shallow Nets | IFT6266
https://legaultma.wordpress.com/2014/04/30/shallow-nets
Simple (naive) feed forward net with 1 hidden layer (this blog post). Actual MLP with more hidden layers. MLP with phonetic information (sequence of phonemes in the current signal window). MLP with phonetic information and pre-training with either a contrastive auto-encoder or denoising auto-encoder. Hopefully, these milestones will help structure and guide my experiments while allowing me to progress towards better solutions. Here, I will describe my first naive attempts at generating speech using the.
May | 2014 | IFT6266
https://legaultma.wordpress.com/2014/05
Month: May, 2014. May 1, 2014. This is the last step of my predefined itinerary. I implemented a denoising autoencoder class and integrated it with the. Class in order to allow initialization of hidden layer weights by unsupervised pre-training. This was pretty straight forward. I started from the. Class and adjusted the learning function and the output function to account for the tied weight structure. May 1, 2014. Probabilistic exploration of hyperparameters. Update the mean of the distribution for eve...
April | 2014 | IFT6266
https://legaultma.wordpress.com/2014/04
Month: April, 2014. April 30, 2014. Phonetic Information and Deeper Nets. This post covers the two next points in my plan (see. Post) I will describe how I integrated the phoneme information in my model, as many different approaches can be used and I will talk about my experience with deeper nets. First, I want to describe the basic layout of the tools I have written so far. I wrote a. Script that uses the previously used. Class This trainer can be used as an executable and takes the following arguments:.
Welcome | IFT6266
https://legaultma.wordpress.com/2014/01/13/welcome
This is the blog I will use to track my progress while taking the IFT 6266. About representation learning algorithms. You can find out more about me on the About. January 13, 2014. Leave a Reply Cancel reply. Enter your comment here. Fill in your details below or click an icon to log in:. Address never made public). You are commenting using your WordPress.com account. ( Log Out. You are commenting using your Twitter account. ( Log Out. You are commenting using your Facebook account. ( Log Out.
Autoencoders for pretraining | IFT6266
https://legaultma.wordpress.com/2014/05/01/autoencoders-for-pretraining
This is the last step of my predefined itinerary. I implemented a denoising autoencoder class and integrated it with the. Class in order to allow initialization of hidden layer weights by unsupervised pre-training. This was pretty straight forward. I started from the. Class and adjusted the learning function and the output function to account for the tied weight structure. When comparing the performance of networks with pre-training to previously trained networks, the results are disappointing:.
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Mar13 | Representation Learning - ift6266h14
https://ift6266h14.wordpress.com/2014/03/11/mar13/comment-page-1
Representation Learning – ift6266h14. Yoshua Bengio's graduate class on representation learning and deep learning. Final Exam →. Posted by Yoshua Bengio. Please study the following material in preparation for the March 13th class:. Of book draft on Deep Learning. Directory includes bibliography) by Y. Bengio, I. Goodfellow and A. Courville. Please put up your questions below, as replies to this post. 33 thoughts on “ Mar13. March 11, 2014 at 10:03. March 11, 2014 at 11:10. March 11, 2014 at 23:39. You ca...
Mar27 | Representation Learning - ift6266h14
https://ift6266h14.wordpress.com/2014/03/23/mar27
Representation Learning – ift6266h14. Yoshua Bengio's graduate class on representation learning and deep learning. Posted by Yoshua Bengio. Please study the following material (on auto-encoders) in preparation for the March 27th class:. Section 7 of Representation Learning: A Review and New Perspectives. By Y Bengio, A. Courville and P. Vincent. Videos 6.1 to 6.7 (Auto-encoders) from Hugo Larochelle’s course on neural networks. Please put up your questions below, as replies to this post. 1 what is it?
Apr3 | Representation Learning - ift6266h14
https://ift6266h14.wordpress.com/2014/03/31/apr3
Representation Learning – ift6266h14. Yoshua Bengio's graduate class on representation learning and deep learning. Apr10 – Exam →. Posted by Yoshua Bengio. Please study the following material (on auto-encoders) in preparation for the April 3rd class:. Better mixing via deep representations. ICML’2013) by Y. Bengio, Grégoire Mesnil, Yann Dauphin and Salah Rifai. Generalized denoising auto-encoders as generative models. NIPS’2013) by Y. Bengio, Yao Li, Guillaume Alain and Pascal Vincent. Disentangling give...
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IFT6266 | Representation Learning
May 1, 2014. This is the last step of my predefined itinerary. I implemented a denoising autoencoder class and integrated it with the. Class in order to allow initialization of hidden layer weights by unsupervised pre-training. This was pretty straight forward. I started from the. Class and adjusted the learning function and the output function to account for the tied weight structure. May 1, 2014. Probabilistic exploration of hyperparameters. Update the mean of the distribution for every parameter by &#...
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