2min chapter

Machine Learning Street Talk (MLST) cover image

ICLR 2020: Yann LeCun and Energy-Based Models

Machine Learning Street Talk (MLST)

CHAPTER

Regularized Latent Variable Methods

All the classical agorisms can be interpreted inthe context of eneto days journey. I'm a little bit sceptical about gans. It just seems to me that we don't need them. So in the context of spars cooding, you leneally reconstruct a factor by finding a factor, a latent factor, that is sparse and minimize clar regularizer r the edi norm. And then you can train the decoder to maximately reconstruct a training samples.

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