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Notes on Deep Learning Theory
Eugene A. Golikov
These are the notes for the lectures that I was giving during Fall 2020 at the Moscow Institute of Physics and Technology (MIPT) and at the Yandex School of Data Analysis (YSDA). T…
Dynamically Stable Infinite-Width Limits of Neural Classifiers
Eugene A. Golikov
Recent research has been focused on two different approaches to studying neural networks training in the limit of infinite width (1) a mean-field (MF) and (2) a constant neural tan…
An Essay on Optimization Mystery of Deep Learning
Eugene Golikov
Despite the huge empirical success of deep learning, theoretical understanding of neural networks learning process is still lacking. This is the reason, why some of its features se…
Embedding-reparameterization procedure for manifold-valued latent variables in generative models
Eugene Golikov, Maksim Kretov
Conventional prior for Variational Auto-Encoder (VAE) is a Gaussian distribution. Recent works demonstrated that choice of prior distribution affects learning capacity of VAE model…
Using stochastic computation graphs formalism for optimization of sequence-to-sequence model
Eugene Golikov, Vlad Zhukov, Maksim Kretov
Variety of machine learning problems can be formulated as an optimization task for some (surrogate) loss function. Calculation of loss function can be viewed in terms of stochastic…