| A guide to deep learning in healthcare A Esteva, A Robicquet, B Ramsundar, V Kuleshov, M DePristo, K Chou, ... Nature medicine 25 (1), 24-29, 2019 | 3514 | 2019 |
| Accurate uncertainties for deep learning using calibrated regression V Kuleshov, N Fenner, S Ermon International conference on machine learning, 2796-2804, 2018 | 729 | 2018 |
| Algorithms for multi-armed bandit problems V Kuleshov, D Precup arXiv preprint arXiv:1402.6028, 2014 | 598 | 2014 |
| Audio super resolution using neural networks V Kuleshov, SZ Enam, S Ermon arXiv preprint arXiv:1708.00853, 2017 | 232* | 2017 |
| Whole-genome haplotyping using long reads and statistical methods V Kuleshov, D Xie, R Chen, D Pushkarev, Z Ma, T Blauwkamp, M Kertesz, ... Nature biotechnology 32 (3), 261-266, 2014 | 228 | 2014 |
| Calibrated structured prediction V Kuleshov, PS Liang Advances in Neural Information Processing Systems 28, 2015 | 144 | 2015 |
| Synthetic long-read sequencing reveals intraspecies diversity in the human microbiome V Kuleshov, C Jiang, W Zhou, F Jahanbani, S Batzoglou, M Snyder Nature biotechnology 34 (1), 64-69, 2016 | 121 | 2016 |
| Quip: 2-bit quantization of large language models with guarantees J Chee, Y Cai, V Kuleshov, CM De Sa Advances in Neural Information Processing Systems 36, 2024 | 110 | 2024 |
| Tensor factorization via matrix factorization V Kuleshov, A Chaganty, P Liang Artificial Intelligence and Statistics, 507-516, 2015 | 100 | 2015 |
| Adversarial examples for natural language classification problems V Kuleshov, S Thakoor, T Lau, S Ermon | 99 | 2018 |
| Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. S Birnbaum, V Kuleshov, Z Enam, PW Koh, S Ermon Advances in Neural Information Processing Systems 32, 2019 | 71 | 2019 |
| Probabilistic single-individual haplotyping V Kuleshov Bioinformatics 30 (17), i379-i385, 2014 | 71 | 2014 |
| Text embeddings reveal (almost) as much as text JX Morris, V Kuleshov, V Shmatikov, AM Rush arXiv preprint arXiv:2310.06816, 2023 | 64 | 2023 |
| Calibrated model-based deep reinforcement learning A Malik, V Kuleshov, J Song, D Nemer, H Seymour, S Ermon International Conference on Machine Learning, 4314-4323, 2019 | 63 | 2019 |
| Harnessing biomedical literature to calibrate clinicians’ trust in AI decision support systems Q Yang, Y Hao, K Quan, S Yang, Y Zhao, V Kuleshov, F Wang Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems …, 2023 | 59 | 2023 |
| Inverse game theory: Learning utilities in succinct games V Kuleshov, O Schrijvers Web and Internet Economics: 11th International Conference, WINE 2015 …, 2015 | 59 | 2015 |
| Estimating uncertainty online against an adversary V Kuleshov, S Ermon Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 50* | 2017 |
| Caduceus: Bi-directional equivariant long-range dna sequence modeling Y Schiff, CH Kao, A Gokaslan, T Dao, A Gu, V Kuleshov arXiv preprint arXiv:2403.03234, 2024 | 46 | 2024 |
| Genome assembly from synthetic long read clouds V Kuleshov, MP Snyder, S Batzoglou Bioinformatics 32 (12), i216-i224, 2016 | 45 | 2016 |
| Neural variational inference and learning in undirected graphical models V Kuleshov, S Ermon Advances in Neural Information Processing Systems 30, 2017 | 44 | 2017 |
Stefano ErmonStanford UniversityZweryfikowany adres z cs.stanford.edu
