Joint Optimization of Segmentation and Color Clustering. - Ekaterina Lobacheva; Olga Veksler; Yuri Boykov (ICCV, 2015)

Deep Part-Based Generative Shape Model with Latent Variables. - Alexander Kirillov; Mikhail Gavrikov; Ekaterina Lobacheva; Anton Osokin; Dmitry P. Vetrov (BMVC, 2016)

Semantic embeddings for program behaviour patterns. - Alexander Chistyakov; Ekaterina Lobacheva; Arseny Kuznetsov; Alexey Romanenko (ICLR (Workshop), 2017)

Bayesian Sparsification of Recurrent Neural Networks. - Ekaterina Lobacheva; Nadezhda Chirkova; Dmitry P. Vetrov (CoRR, 2017)

Bayesian Compression for Natural Language Processing. - Nadezhda Chirkova; Ekaterina Lobacheva; Dmitry P. Vetrov (EMNLP, 2018)

Monotonic models for real-time dynamic malware detection. - Alexander Chistyakov; Ekaterina Lobacheva; Alexander Shevelev; Alexey Romanenko (ICLR (Workshop), 2018)

Semantic embeddings for program behavior patterns. - Alexander Chistyakov; Ekaterina Lobacheva; Arseny Kuznetsov; Alexey Romanenko (CoRR, 2018)

Bayesian Sparsification of Gated Recurrent Neural Networks. - Ekaterina Lobacheva; Nadezhda Chirkova; Dmitry P. Vetrov (CoRR, 2018)

Structured Sparsification of Gated Recurrent Neural Networks. - Ekaterina Lobacheva; Nadezhda Chirkova; Alexander Markovich; Dmitry P. Vetrov (AAAI, 2020)

On Power Laws in Deep Ensembles. - Ekaterina Lobacheva; Nadezhda Chirkova; Maxim Kodryan; Dmitry P. Vetrov (NeurIPS, 2020)

Deep Ensembles on a Fixed Memory Budget: One Wide Network or Several Thinner Ones? - Nadezhda Chirkova; Ekaterina Lobacheva; Dmitry P. Vetrov (CoRR, 2020)

On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay. - Ekaterina Lobacheva; Maxim Kodryan; Nadezhda Chirkova; Andrey Malinin; Dmitry P. Vetrov (NeurIPS, 2021)

On the Memorization Properties of Contrastive Learning. - Ildus Sadrtdinov; Nadezhda Chirkova; Ekaterina Lobacheva (CoRR, 2021)

Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems. - Evgeny Bobrov; Sergey Troshin 001; Nadezhda Chirkova; Ekaterina Lobacheva; Sviatoslav Panchenko; Dmitry P. Vetrov; Dmitry Kropotov (CoRR, 2021)

Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes. - Maxim Kodryan; Ekaterina Lobacheva; Maksim Nakhodnov; Dmitry P. Vetrov (NeurIPS, 2022)

To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning. - Ildus Sadrtdinov; Dmitrii Pozdeev; Dmitry P. Vetrov; Ekaterina Lobacheva (NeurIPS, 2023)

Large Learning Rates Improve Generalization: But How Large Are We Talking About? - Ekaterina Lobacheva; Eduard Pockonechnyy; Maxim Kodryan; Dmitry P. Vetrov (CoRR, 2023)

Where Do Large Learning Rates Lead Us? - Ildus Sadrtdinov; Maxim Kodryan; Eduard Pokonechny; Ekaterina Lobacheva; Dmitry P. Vetrov (NeurIPS, 2024)

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning. - Andrei Mircea; Supriyo Chakraborty; Nima Chitsazan; Irina Rish; Ekaterina Lobacheva (ACL (1), 2025)

SGD as Free Energy Minimization: A Thermodynamic View on Neural Network Training. - Ildus Sadrtdinov; Ivan Klimov; Ekaterina Lobacheva; Dmitry Vetrov (CoRR, 2025)