Survey on Efficient Training of Large Neural Networks. - Julia Gusak; Daria Cherniuk; Alena Shilova; Alexandr Katrutsa; Daniel Bershatsky; Xunyi Zhao; Lionel Eyraud-Dubois; Oleh Shliazhko; Denis Dimitrov; Ivan V. Oseledets; Olivier Beaumont (IJCAI, 2022)

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

Survey on Large Scale Neural Network Training. - Julia Gusak; Daria Cherniuk; Alena Shilova; Alexandr Katrutsa; Daniel Bershatsky; Xunyi Zhao; Lionel Eyraud-Dubois; Oleg Shlyazhko; Denis Dimitrov; Ivan V. Oseledets; Olivier Beaumont (CoRR, 2022)

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

Few-bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction. - Georgii Sergeevich Novikov; Daniel Bershatsky; Julia Gusak; Alex Shonenkov; Denis Valerievich Dimitrov; Ivan V. Oseledets (ICML, 2023)

LoTR: Low Tensor Rank Weight Adaptation. - Daniel Bershatsky; Daria Cherniuk; Talgat Daulbaev; Aleksandr Mikhalev; Ivan V. Oseledets (CoRR, 2024)

Federated privacy-preserving collaborative filtering for on-device next app prediction. - Albert Saiapin; Gleb Balitskiy; Daniel Bershatsky; Aleksandr Katrutsa; Evgeny Frolov; Alexey A. Frolov; Ivan V. Oseledets; Vitaliy Kharin (User Model. User Adapt. Interact., 2024)

On the Spatial Structure of Mixture-of-Experts in Transformers. - Daniel Bershatsky; Ivan V. Oseledets (CoRR, 2025)