| 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 | 2424 | 2019 |
| Accurate uncertainties for deep learning using calibrated regression V Kuleshov, N Fenner, S Ermon International conference on machine learning, 2796-2804, 2018 | 556 | 2018 |
| Algorithms for multi-armed bandit problems V Kuleshov, D Precup arXiv preprint arXiv:1402.6028, 2014 | 500 | 2014 |
| 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 | 220 | 2014 |
| Audio super resolution using neural networks V Kuleshov, SZ Enam, S Ermon arXiv preprint arXiv:1708.00853, 2017 | 199* | 2017 |
| 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 | 120 | 2016 |
| Calibrated structured prediction V Kuleshov, PS Liang Advances in Neural Information Processing Systems 28, 2015 | 117 | 2015 |
| Tensor factorization via matrix factorization V Kuleshov, A Chaganty, P Liang Artificial Intelligence and Statistics, 507-516, 2015 | 93 | 2015 |
| Adversarial examples for natural language classification problems V Kuleshov, S Thakoor, T Lau, S Ermon | 85* | 2018 |
| Probabilistic single-individual haplotyping V Kuleshov Bioinformatics 30 (17), i379-i385, 2014 | 69 | 2014 |
| 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 | 51 | 2019 |
| Inverse game theory: Learning utilities in succinct games V Kuleshov, O Schrijvers Web and Internet Economics: 11th International Conference, WINE 2015 …, 2015 | 50 | 2015 |
| 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 | 44 | 2019 |
| Estimating uncertainty online against an adversary V Kuleshov, S Ermon Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 43* | 2017 |
| Neural variational inference and learning in undirected graphical models V Kuleshov, S Ermon Advances in Neural Information Processing Systems 30, 2017 | 41 | 2017 |
| Genome assembly from synthetic long read clouds V Kuleshov, MP Snyder, S Batzoglou Bioinformatics 32 (12), i216-i224, 2016 | 40 | 2016 |
| A machine-compiled database of genome-wide association studies V Kuleshov, J Ding, C Vo, B Hancock, A Ratner, Y Li, C Ré, S Batzoglou, ... Nature communications 10 (1), 3341, 2019 | 36 | 2019 |
| Learning with weak supervision from physics and data-driven constraints H Ren, R Stewart, J Song, V Kuleshov, S Ermon AI Magazine 39 (1), 27-38, 2018 | 33 | 2018 |
| Deep hybrid models: Bridging discriminative and generative approaches V Kuleshov, S Ermon Proceedings of the Conference on Uncertainty in AI (UAI), 2017 | 30 | 2017 |
| Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration V Kuleshov International Conference on Machine Learning, 1418-1425, 2013 | 27 | 2013 |
Stefano ErmonStanford UniversityZweryfikowany adres z cs.stanford.edu
