| A critical look at the evaluation of GNNs under heterophily: Are we really making progress? O Platonov, D Kuznedelev, M Diskin, A Babenko, L Prokhorenkova arXiv preprint arXiv:2302.11640, 2023 | 123 | 2023 |
| Spqr: A sparse-quantized representation for near-lossless llm weight compression T Dettmers, R Svirschevski, V Egiazarian, D Kuznedelev, E Frantar, ... arXiv preprint arXiv:2306.03078, 2023 | 97 | 2023 |
| Influence of relativistic rotation on the confinement-deconfinement transition in gluodynamics VV Braguta, AY Kotov, DD Kuznedelev, AA Roenko Physical Review D 103 (9), 094515, 2021 | 54 | 2021 |
| Study of the confinement/deconfinement phase transition in rotating lattice SU (3) gluodynamics VV Braguta, AY Kotov, DD Kuznedelev, AA Roenko JETP Letters 112, 6-12, 2020 | 34 | 2020 |
| Characterizing graph datasets for node classification: Beyond homophily-heterophily dichotomy O Platonov, D Kuznedelev, A Babenko, L Prokhorenkova arXiv preprint arXiv:2209.06177 21, 2022 | 28 | 2022 |
| Lattice study of QCD at finite chiral density: topology and confinement N Astrakhantsev, VV Braguta, AY Kotov, DD Kuznedelev, AA Nikolaev The European Physical Journal A 57 (1), 15, 2021 | 19 | 2021 |
| Extreme compression of large language models via additive quantization V Egiazarian, A Panferov, D Kuznedelev, E Frantar, A Babenko, D Alistarh arXiv preprint arXiv:2401.06118, 2024 | 15 | 2024 |
| Characterizing graph datasets for node classification: Homophily-heterophily dichotomy and beyond O Platonov, D Kuznedelev, A Babenko, L Prokhorenkova Advances in Neural Information Processing Systems 36, 2024 | 13 | 2024 |
| Lattice study of the confinement/deconfinement transition in rotating gluodynamics VV Braguta, AY Kotov, DD Kuznedelev, AA Roenko arXiv preprint arXiv:2110.12302, 2021 | 13 | 2021 |
| Sparse finetuning for inference acceleration of large language models E Kurtic, D Kuznedelev, E Frantar, M Goin, D Alistarh arXiv preprint arXiv:2310.06927, 2023 | 6 | 2023 |
| Lattice study of QCD properties under extreme conditions: temperature, density, rotation, and magnetic field NY Astrakhantsev, VV Braguta, NV Kolomoyets, AY Kotov, ... Physics of Particles and Nuclei 52, 536-541, 2021 | 6 | 2021 |
| Accurate neural network pruning requires rethinking sparse optimization D Kuznedelev, E Kurtic, E Iofinova, E Frantar, A Peste, D Alistarh arXiv preprint arXiv:2308.02060, 2023 | 4 | 2023 |
| A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta M Velikanov, D Kuznedelev, D Yarotsky arXiv preprint arXiv:2206.11124, 2022 | 4 | 2022 |
| Evaluating robustness and uncertainty of graph models under structural distributional shifts G Bazhenov, D Kuznedelev, A Malinin, A Babenko, L Prokhorenkova Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
| PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression V Malinovskii, D Mazur, I Ilin, D Kuznedelev, K Burlachenko, K Yi, ... arXiv preprint arXiv:2405.14852, 2024 | 2 | 2024 |
| Cap: Correlation-aware pruning for highly-accurate sparse vision models D Kuznedelev, E Kurtić, E Frantar, D Alistarh Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| ovit: An accurate second-order pruning framework for vision transformers D Kuznedelev, E Kurtic, E Frantar, D Alistarh | 2 | 2022 |
| Does Diffusion Beat GAN in Image Super Resolution? D Kuznedelev, V Startsev, D Shlenskii, S Kastryulin arXiv preprint arXiv:2405.17261, 2024 | | 2024 |
| YaART: Yet Another ART Rendering Technology S Kastryulin, A Konev, A Shishenya, E Lyapustin, A Khurshudov, ... arXiv preprint arXiv:2404.05666, 2024 | | 2024 |
| Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression D Kuznedelev, S Tabesh, K Noorbakhsh, E Frantar, S Beery, E Kurtic, ... arXiv preprint arXiv:2303.14409, 2023 | | 2023 |
