Eugene A. Golikov

According to our database, Eugene A. Golikov authored at least 13 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

A Generalization Bound for Nearly-Linear Networks.

Eugene A. Golikov

Trans. Mach. Learn. Res., 2025

2022

A review of neural architecture search.

Dilyara Baymurzina, Eugene A. Golikov, Mikhail Burtsev

Neurocomputing, 2022

Neural Tangent Kernel: A Survey.

Eugene A. Golikov, Eduard Pokonechny, Vladimir Korviakov

CoRR, 2022

Feature Learning in L₂-regularized DNNs: Attraction/Repulsion and Sparsity.

Arthur Jacot, Eugene A. Golikov, Clément Hongler, Franck Gabriel

CoRR, 2022

Feature Learning in $L_2$-regularized DNNs: Attraction/Repulsion and Sparsity.

Arthur Jacot, Eugene A. Golikov, Clément Hongler, Franck Gabriel

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Non-Gaussian Tensor Programs.

Eugene A. Golikov, Greg Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2020

Notes on Deep Learning Theory.

Eugene A. Golikov

CoRR, 2020

Dynamically Stable Infinite-Width Limits of Neural Classifiers.

Eugene A. Golikov

CoRR, 2020

Towards a General Theory of Infinite-Width Limits of Neural Classifiers.

Eugene A. Golikov

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Quadratic number of nodes is sufficient to learn a dataset via gradient descent.

Biswarup Das, Eugene A. Golikov

CoRR, 2019

An Essay on Optimization Mystery of Deep Learning.

Eugene A. Golikov

CoRR, 2019

2018

Embedding-reparameterization procedure for manifold-valued latent variables in generative models.

Eugene A. Golikov, Maksim Kretov

CoRR, 2018

2017

Using stochastic computation graphs formalism for optimization of sequence-to-sequence model.

Eugene A. Golikov, Vlad Zhukov, Maksim Kretov

CoRR, 2017