| Multimodal generative models for scalable weakly-supervised learning M Wu, N Goodman Advances in neural information processing systems 31, 2018 | 423 | 2018 |
| Beyond sparsity: Tree regularization of deep models for interpretability M Wu, M Hughes, S Parbhoo, M Zazzi, V Roth, F Doshi-Velez Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 309 | 2018 |
| On mutual information in contrastive learning for visual representations M Wu, C Zhuang, M Mosse, D Yamins, N Goodman arXiv preprint arXiv:2005.13149, 2020 | 90 | 2020 |
| Conditional negative sampling for contrastive learning of visual representations M Wu, M Mosse, C Zhuang, D Yamins, N Goodman arXiv preprint arXiv:2010.02037, 2020 | 84 | 2020 |
| Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database M Wu, M Ghassemi, M Feng, LA Celi, P Szolovits, F Doshi-Velez Journal of the American Medical Informatics Association 24 (3), 488-495, 2017 | 72 | 2017 |
| Predicting intervention onset in the ICU with switching state space models M Ghassemi, M Wu, MC Hughes, P Szolovits, F Doshi-Velez AMIA Summits on Translational Science Proceedings 2017, 82, 2017 | 71 | 2017 |
| Viewmaker networks: Learning views for unsupervised representation learning A Tamkin, M Wu, N Goodman arXiv preprint arXiv:2010.07432, 2020 | 68 | 2020 |
| Variational item response theory: Fast, accurate, and expressive M Wu, RL Davis, BW Domingue, C Piech, N Goodman arXiv preprint arXiv:2002.00276, 2020 | 64 | 2020 |
| Zero shot learning for code education: Rubric sampling with deep learning inference M Wu, M Mosse, N Goodman, C Piech Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 782-790, 2019 | 64 | 2019 |
| Pragmatic inference and visual abstraction enable contextual flexibility during visual communication JE Fan, RD Hawkins, M Wu, ND Goodman Computational Brain & Behavior 3 (1), 86-101, 2020 | 50 | 2020 |
| Regional tree regularization for interpretability in deep neural networks M Wu, S Parbhoo, M Hughes, R Kindle, L Celi, M Zazzi, V Roth, ... Proceedings of the AAAI conference on artificial intelligence 34 (04), 6413-6421, 2020 | 41 | 2020 |
| Meta-amortized variational inference and learning M Wu, K Choi, N Goodman, S Ermon Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6404-6412, 2020 | 36* | 2020 |
| Optimizing for interpretability in deep neural networks with tree regularization M Wu, S Parbhoo, MC Hughes, V Roth, F Doshi-Velez Journal of Artificial Intelligence Research 72, 1-37, 2021 | 27 | 2021 |
| Temperature as uncertainty in contrastive learning O Zhang, M Wu, J Bayrooti, N Goodman arXiv preprint arXiv:2110.04403, 2021 | 25 | 2021 |
| Prototransformer: A meta-learning approach to providing student feedback M Wu, N Goodman, C Piech, C Finn arXiv preprint arXiv:2107.14035, 2021 | 24 | 2021 |
| Tutela: An open-source tool for assessing user-privacy on ethereum and tornado cash M Wu, W McTighe, K Wang, IA Seres, N Bax, M Puebla, M Mendez, ... arXiv preprint arXiv:2201.06811, 2022 | 20 | 2022 |
| Generative grading: near human-level accuracy for automated feedback on richly structured problems A Malik, M Wu, V Vasavada, J Song, M Coots, J Mitchell, N Goodman, ... arXiv preprint arXiv:1905.09916, 2019 | 17 | 2019 |
| Harpervalleybank: A domain-specific spoken dialog corpus M Wu, J Nafziger, A Scodary, A Maas arXiv preprint arXiv:2010.13929, 2020 | 16 | 2020 |
| Differentiable antithetic sampling for variance reduction in stochastic variational inference M Wu, N Goodman, S Ermon The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 16 | 2019 |
| Multimodal generative models for compositional representation learning M Wu, N Goodman arXiv preprint arXiv:1912.05075, 2019 | 15 | 2019 |
Noah D. GoodmanStanford University在 stanford.edu 的电子邮件经过验证
