Optimal Control and Sensitivity Analysis for Two Risk Models. - Ekaterina Bulinskaya; Julia Gusak (Commun. Stat. Simul. Comput., 2016)

Automated Multi-Stage Compression of Neural Networks. - Julia Gusak; Maksym Kholyavchenko; Evgeny Ponomarev; Larisa Markeeva; Philip Blagoveschensky; Andrzej Cichocki; Ivan V. Oseledets (ICCV Workshops, 2019)

One time is not enough: iterative tensor decomposition for neural network compression. - Julia Gusak; Maksym Kholyavchenko; Evgeny Ponomarev; Larisa Markeeva; Ivan V. Oseledets; Andrzej Cichocki (CoRR, 2019)

Reduced-Order Modeling of Deep Neural Networks. - Talgat Daulbaev; Julia Gusak; Evgeny Ponomarev; Andrzej Cichocki; Ivan V. Oseledets (CoRR, 2019)

Stable Low-Rank Tensor Decomposition for Compression of Convolutional Neural Network. - Anh Huy Phan 001; Konstantin Sobolev; Konstantin Sozykin; Dmitry Ermilov; Julia Gusak; Petr Tichavský; Valeriy Glukhov; Ivan V. Oseledets; Andrzej Cichocki (ECCV (29), 2020)

Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs. - Talgat Daulbaev; Alexandr Katrutsa; Larisa Markeeva; Julia Gusak; Andrzej Cichocki; Ivan V. Oseledets (NeurIPS, 2020)

Interpolated Adjoint Method for Neural ODEs. - Talgat Daulbaev; Alexandr Katrutsa; Larisa Markeeva; Julia Gusak; Andrzej Cichocki; Ivan V. Oseledets (CoRR, 2020)

Towards Understanding Normalization in Neural ODEs. - Julia Gusak; Larisa Markeeva; Talgat Daulbaev; Alexandr Katrutsa; Andrzej Cichocki; Ivan V. Oseledets (CoRR, 2020)

Active Subspace of Neural Networks: Structural Analysis and Universal Attacks. - Chunfeng Cui; Kaiqi Zhang 002; Talgat Daulbaev; Julia Gusak; Ivan V. Oseledets; Zheng Zhang 005 (SIAM J. Math. Data Sci., 2020)

Meta-Solver for Neural Ordinary Differential Equations. - Julia Gusak; Alexandr Katrutsa; Talgat Daulbaev; Andrzej Cichocki; Ivan V. Oseledets (CoRR, 2021)

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)

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)

Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch. - Xunyi Zhao; Théotime Le Hellard; Lionel Eyraud-Dubois; Julia Gusak; Olivier Beaumont (ICML, 2023)

Efficient GPT Model Pre-training using Tensor Train Matrix Representation. - Viktoria Chekalina; Georgiy Novikov; Julia Gusak; Alexander Panchenko; Ivan V. Oseledets (PACLIC, 2023)

Efficient GPT Model Pre-training using Tensor Train Matrix Representation. - Viktoria Chekalina; Georgii S. Novikov; Julia Gusak; Ivan V. Oseledets; Alexander Panchenko (CoRR, 2023)

Quantization Aware Factorization for Deep Neural Network Compression. - Daria Cherniuk; Stanislav Abukhovich; Anh Huy Phan 001; Ivan V. Oseledets; Andrzej Cichocki; Julia Gusak (J. Artif. Intell. Res., 2024)