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March | 2014 | IFT6266 Project on Representation Learning
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IFT6266 Project on Representation Learning. A great WordPress.com site. Monthly Archives: March 2014. Second MLP architecture for next sample prediction Part III. March 31, 2014. This time, I used the code of my previous post. For a comparison of results on FCJF0. Additionally, as compared to the previous post, the training curves are much smoother using this learning rate. The best test error (mean-squared error) for this run was 2809.36 unnormalized, or 0.00896 normalized. Learning rate: 0.0025. Https:...
May | 2014 | IFT6266 Project on Representation Learning
https://ift6266hjb.wordpress.com/2014/05
IFT6266 Project on Representation Learning. A great WordPress.com site. Monthly Archives: May 2014. Results with ReLUs and different subjects. May 1, 2014. I ran some more experiments based on my previous post:. Using ReLUs on the hidden layers. Using more hidden units in the NSNN. Training/generating with other speakers. 1 Using ReLUs on the hidden layers. NSNN: 2 hidden layers (200 and 150 units). WNN: 1 hidden layer (250 units). Number of epochs: 500. The training procedure stopped improving the train...
February | 2014 | IFT6266 Project on Representation Learning
https://ift6266hjb.wordpress.com/2014/02
IFT6266 Project on Representation Learning. A great WordPress.com site. Monthly Archives: February 2014. Second MLP architecture for next sample prediction. February 27, 2014. Except for the fact that its parameters are actually the output of the first network. It will nonetheless receive a window of samples as input, and output the predicted next sample. Here is a visual description of what I have in mind:. Perform back propagation on the NSNN using the output of WNN as weights, the window of current sa...
Results with 2-layer MLP on 1 speaker | IFT6266 Project on Representation Learning
https://ift6266hjb.wordpress.com/2014/04/27/results-with-2-layer-mlp-on-1-speaker
IFT6266 Project on Representation Learning. A great WordPress.com site. Results with 2-layer MLP on 1 speaker. April 27, 2014. I used an improved implementation of the previous unified architecture. To generate speech on subject FCJF0. The first 9 utterances were used for train/valid/test, and the 10. Utterance was kept for generation. The hyperparameters were:. 2 hidden layers (200 and 150 units) for the network outputting the next sample (NSNN). Number of epochs: 500. Here are the training curves:.
Second MLP architecture for next sample prediction | IFT6266 Project on Representation Learning
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IFT6266 Project on Representation Learning. A great WordPress.com site. Second MLP architecture for next sample prediction. February 27, 2014. Except for the fact that its parameters are actually the output of the first network. It will nonetheless receive a window of samples as input, and output the predicted next sample. Here is a visual description of what I have in mind:. We could use a slightly modified back propagation algorithm for this model. It would work in a loop of three steps:. Perform back ...
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Mar13 | Representation Learning - ift6266h14
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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
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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?
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Experiments in Representation Learning | My journal for IFT6266
Experiments in Representation Learning. My journal for IFT6266. Skip to primary content. Skip to secondary content. Stacking RBMs and Training on All the Data. May 10, 2013. Adding a second hidden layer reduced the test RMSE substantially, so I added a third hidden layer in exactly the same way, again using 500 hidden units. These results, by far my best yet, indicate that layer-wise unsupervised pre-training was very effective, even though I did not spend much time fine-tuning the hyper-parameters.
Representation Learning | Course material for graduate class ift6266h13
Course material for graduate class ift6266h13. Instructor: Professor Yoshua Bengio. Teaching assistant: PhD candidate Ian Goodfellow. Université de Montréal, département d’informatique et recherche opérationnelle. Course plan ( pdf. Class hours and locations:. Mondays 2:30-4:30pm, Z-260. Thursdays 9:30-11:30am, Z-260. Responses to “”. Feed for this Entry. Leave a Reply Cancel reply. Enter your comment here. Please log in using one of these methods to post your comment:. Address never made public).
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Machine Learning on Emotion Recognition | Research Journal of Yangyang Zhao for Machine Learning Course
Machine Learning on Emotion Recognition. Research Journal of Yangyang Zhao for Machine Learning Course. Second attempt with unsupervised pretraining. May 11, 2013. After the previous failure, I change my strategy: I try to train each layer separately, try to find the best models parameter. For the first layer, I try 2304 units gRBM, 576 units gRBM ,. 2304 units DAE, 576 units DAE. For the second layer, I use 576 hidden units gRBM. The minimum construction error is 195.316. 2304 hidden units gRBM layer fo...
Representation Learning - ift6266h14 | Yoshua Bengio's graduate class on representation learning and deep learning
Representation Learning – ift6266h14. Yoshua Bengio's graduate class on representation learning and deep learning. Web site for the graduate class on Representation Learning Algorithms IFT6266 H14. Instructor: Professor Yoshua Bengio. Teaching assistant: PhD candidate David Warde-Farley. Université de Montréal, département d’informatique et recherche opérationnelle. Course plan ( pdf. Class hours and locations:. Mondays 2:30-4:30pm, Z-210. Thursdays 9:30-11:30am, 1409 PAA. One thought on “ Home. Yoshua B...
IFT6266 – H2015 Representation Learning – A mostly deep learning course
IFT6266 – H2015 Representation Learning. A mostly deep learning course. Project Blogs and Repos. This is a course on representation learning in general and deep learning in particular. Deep learning has recently been responsible for a large number of impressive empirical gains across a wide array of applications including most dramatically in object recognition and detection in images and speech recognition. Département d’informatique et recherche opérationnelle (DIRO). PhD student Vincent Dumoulin.
IFT6266 Project on Representation Learning | A great WordPress.com site
IFT6266 Project on Representation Learning. A great WordPress.com site. Results with ReLUs and different subjects. May 1, 2014. I ran some more experiments based on my previous post:. Using ReLUs on the hidden layers. Using more hidden units in the NSNN. Training/generating with other speakers. 1 Using ReLUs on the hidden layers. NSNN: 2 hidden layers (200 and 150 units). WNN: 1 hidden layer (250 units). Number of epochs: 500. This effect was found to disappear after around 75 epochs. The training proced...
Sina Honari's blog for Representation Learning | My blog on the Representation Learning Course
Sina Honari's blog for Representation Learning. My blog on the Representation Learning Course. Skip to primary content. Skip to secondary content. Fine tuning of unsupervised pretraining. May 11, 2013. Here are the results for the model with GCN preprocessing. In both experiments I had to set the learning rate to 1e-5. The training algorithm diverged using higher values of the learning rates . As the results demonstrate, sigmoid units work better with GCN. May 10, 2013. In the next attempt, I used unsupe...
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Experiments in deep learning for speech synthesis | Jessica Thompson's journal for IFT6266 – Representation Learning at Université de Montréal
Experiments in deep learning for speech synthesis. Jessica Thompson's journal for IFT6266 – Representation Learning at Université de Montréal. Skip to primary content. Skip to secondary content. January 24, 2014. This blog will document my progress on the final project for Dr. Yoshua Bengio’s course on Representation Learning. The task for the project is speech synthesis. April 30, 2014. Vincent published a blog post on h ow to use the Pylearn2 TIMIT class with multiple sources, specifically c. Monitor i...
Speech synthesis experiments | Smile! You’re at the best WordPress.com site ever
You’re at the best WordPress.com site ever. The plan, at this point, is to use a gammatone dictionary and sparse coding to train more efficiently on the TIMIT dataset. Gammatone functions are filters applied on frequencies. The goal is to keep frequencies processed by our ears. Joao explains this greatly in his blog. The idea is to use this sparse-coded version as input of a spike and slab RBM segmented by frames of 160 samples and with the previous, current and next phones. I kept the parameters use...
IFT6266 project | Experiments in speech synthesis with neural networks by Pierre-Luc Vaudry
Experiments in speech synthesis with neural networks by Pierre-Luc Vaudry. Predicting the standard deviation. Hubert Banville successfully implemented the kind of architecture I also had in mind. In this experiment, I wanted to see if having this standard deviation predicted by the neural network could improve the results. I based myself on Hubert’s code, which is described in his last post. To this loss function, the L1 and L2 norm penalties are also added as regulators, as in Hubert’s experiments...
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