davidscottkrueger A blog on Representation Learning and Deep Learning for ift6266h14
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Got a job at DeepMind as a research scientist in AI safety. And presented at ICML and NIPS workshops. Attended the Deep Learning Summer School. Finished and defended PhD thesis. Moved to London and started working at DeepMind. And panel at the Governance of Emerging Technologies conference at ASU.
Web site for the graduate class on Representation Learning Algorithms IFT6266 H14.
So far using more components seems only to improve results. I was able to train models longer and get better results using an exponential decay of 1.
I proposed to use the implementation composed of a SdA and a MLP of 2 hidden layers for each phoneme. I will present in this last post the results of this proposal. The hyperparameters of the implementation are almost the same as the ones used in my final experiment post.
On the RNN implementation based on Vincent Dumoulin implementation. We can see that the prediction error from ground truth varies a lot along the sequence.
Experiments in speech synthesis with neural networks by Pierre-Luc Vaudry. 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.
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The site had the following in the homepage, "A blog on Representation Learning and Deep Learning for ift6266h14." I noticed that the web site stated " This blog is Over! This blog was for my class, which is over." They also stated " I dont post here anymore, and dont intend to. Here is my current school web page, tho, iyi. Neural Networks can be used ."ANALYZE MORE BUSINESSES
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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.
Experiments in speech synthesis with neural networks by Pierre-Luc Vaudry. 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.
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