Empirical study of extreme overfitting points of neural networks. - Daniil Merkulov; Ivan V. Oseledets (CoRR, 2019)

Stochastic gradient algorithms from ODE splitting perspective. - Daniil Merkulov; Ivan V. Oseledets (CoRR, 2020)

Follow the bisector: a simple method for multi-objective optimization. - Alexandr Katrutsa; Daniil Merkulov; Nurislam Tursynbek; Ivan V. Oseledets (CoRR, 2020)

A New Multi-objective Approach to Optimize Irrigation Using a Crop Simulation Model and Weather History. - Mikhail Gasanov; Daniil Merkulov; Artyom Nikitin; Sergey A. Matveev; Nikita Stasenko; Anna Petrovskaia; Mariia Pukalchik; Ivan V. Oseledets (ICCS (4), 2021)

Fast Line Search for Multi-Task Learning. - Andrey Filatov; Daniil Merkulov (CoRR, 2021)

Memory-Efficient Backpropagation through Large Linear Layers. - Daniel Bershatsky; Aleksandr Mikhalev; Alexandr Katrutsa; Julia Gusak; Daniil Merkulov; Ivan V. Oseledets (CoRR, 2022)

NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizers. - Valentin Leplat; Daniil Merkulov; Aleksandr Katrutsa; Daniel Bershatsky; Ivan V. Oseledets (CoRR, 2022)

Quantization of Large Language Models with an Overdetermined Basis. - Daniil Merkulov; Daria Cherniuk; Alexander Rudikov; Ivan V. Oseledets; Ekaterina A. Muravleva; Aleksandr Mikhalev; Boris Kashin (UAI, 2024)