20 citations · 28 across the 10 of their papers we have counts for
10 papers
Neural Tangent Kernel: A Survey
Eugene Golikov, Eduard Pokonechnyy, Vladimir Korviakov
A seminal work [Jacot et al., 2018] demonstrated that training a neural network under specific parameterization is equivalent to performing a particular kernel method as width goes…
Feature Learning in -regularized DNNs: Attraction/Repulsion and Sparsity
Arthur Jacot, Eugene Golikov, Clément Hongler +1
We study the loss surface of DNNs with regularization. We show that the loss in terms of the parameters can be reformulated into a loss in terms of the layerwise activation…
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…
Towards a General Theory of Infinite-Width Limits of Neural Classifiers
Eugene A. Golikov
Obtaining theoretical guarantees for neural networks training appears to be a hard problem in a general case. Recent research has been focused on studying this problem in the limit…
Quadratic number of nodes is sufficient to learn a dataset via gradient descent
Biswarup Das, Eugene. A. Golikov
We prove that if an activation function satisfies some mild conditions and number of neurons in a two-layered fully connected neural network with this activation function is beyond…