| Structured bayesian pruning via log-normal multiplicative noise K Neklyudov, D Molchanov, A Ashukha, DP Vetrov Advances in Neural Information Processing Systems 30, 2017 | 242 | 2017 |
| Performance of machine learning algorithms in predicting game outcome from drafts in dota 2 A Semenov, P Romov, S Korolev, D Yashkov, K Neklyudov International conference on analysis of images, social networks and texts, 26-37, 2016 | 103 | 2016 |
| Uncertainty estimation via stochastic batch normalization A Atanov, A Ashukha, D Molchanov, K Neklyudov, D Vetrov Advances in Neural Networks–ISNN 2019: 16th International Symposium on …, 2019 | 61 | 2019 |
| Action Matching: Learning Stochastic Dynamics from Samples K Neklyudov, R Brekelmans, D Severo, A Makhzani | 51 | 2023 |
| Involutive MCMC: a unifying framework K Neklyudov, M Welling, E Egorov, D Vetrov International Conference on Machine Learning, 7273-7282, 2020 | 44 | 2020 |
| Variance networks: When expectation does not meet your expectations K Neklyudov, D Molchanov, A Ashukha, D Vetrov arXiv preprint arXiv:1803.03764, 2018 | 37 | 2018 |
| Metropolis-Hastings view on variational inference and adversarial training K Neklyudov, E Egorov, P Shvechikov, D Vetrov arXiv preprint arXiv:1810.07151, 2018 | 20 | 2018 |
| Efficient evolutionary search over chemical space with large language models H Wang, M Skreta, CT Ser, W Gao, L Kong, F Strieth-Kalthoff, C Duan, ... arXiv preprint arXiv:2406.16976, 2024 | 16* | 2024 |
| A computational framework for solving wasserstein lagrangian flows K Neklyudov, R Brekelmans, A Tong, L Atanackovic, Q Liu, A Makhzani arXiv preprint arXiv:2310.10649, 2023 | 15 | 2023 |
| Applications of Machine Learning in Dota 2: Literature Review and Practical Knowledge Sharing. AM Semenov, P Romov, K Neklyudov, D Yashkov, D Kireev MLSA@ PKDD/ECML, 2016 | 14 | 2016 |
| Wasserstein quantum Monte Carlo: a novel approach for solving the quantum many-body Schrödinger equation K Neklyudov, J Nys, L Thiede, J Carrasquilla, Q Liu, M Welling, ... Advances in Neural Information Processing Systems 36, 63461-63482, 2023 | 12 | 2023 |
| Orbital mcmc K Neklyudov, M Welling International Conference on Artificial Intelligence and Statistics, 5790-5814, 2022 | 12 | 2022 |
| Diffusion models as constrained samplers for optimization with unknown constraints L Kong, Y Du, W Mu, K Neklyudov, V De Bortoli, D Wu, H Wang, A Ferber, ... arXiv preprint arXiv:2402.18012, 2024 | 11* | 2024 |
| Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling Y Du, M Plainer, R Brekelmans, C Duan, F Noé, CP Gomes, ... arXiv preprint arXiv:2410.07974, 2024 | 10 | 2024 |
| Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC W Lin, F Dangel, R Eschenhagen, K Neklyudov, A Kristiadi, RE Turner, ... arXiv preprint arXiv:2312.05705, 2023 | 9* | 2023 |
| Quantum hypernetworks: Training binary neural networks in quantum superposition J Carrasquilla, M Hibat-Allah, E Inack, A Makhzani, K Neklyudov, ... arXiv preprint arXiv:2301.08292, 2023 | 8 | 2023 |
| The Superposition of Diffusion Models Using the It\^ o Density Estimator M Skreta, L Atanackovic, AJ Bose, A Tong, K Neklyudov arXiv preprint arXiv:2412.17762, 2024 | 5* | 2024 |
| Meta flow matching: Integrating vector fields on the wasserstein manifold L Atanackovic, X Zhang, B Amos, M Blanchette, LJ Lee, Y Bengio, A Tong, ... arXiv preprint arXiv:2408.14608, 2024 | 5 | 2024 |
| Deterministic gibbs sampling via ordinary differential equations K Neklyudov, R Bondesan, M Welling arXiv preprint arXiv:2106.10188, 2021 | 5 | 2021 |
| The Implicit Metropolis-Hastings Algorithm K Neklyudov, E Egorov, D Vetrov Advances in Neural Information Processing Systems, 2019, 2019 | 5 | 2019 |
Dmitry VetrovProfessor of Computer Science at Constructor University, BremenBestätigte E-Mail-Adresse bei constructor.university