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Mike Wu
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Year
Multimodal generative models for scalable weakly-supervised learning
M Wu, N Goodman
Advances in Neural Information Processing Systems 31, 2018
2002018
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
1922018
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
592017
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
542017
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
432020
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
322019
Viewmaker networks: Learning views for unsupervised representation learning
A Tamkin, M Wu, N Goodman
arXiv preprint arXiv:2010.07432, 2020
302020
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
272020
Variational item response theory: Fast, accurate, and expressive
M Wu, RL Davis, BW Domingue, C Piech, N Goodman
arXiv preprint arXiv:2002.00276, 2020
262020
Meta-amortized variational inference and learning
K Choi, M Wu, N Goodman, S Ermon
arXiv preprint arXiv:1902.01950, 2019
26*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
182020
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
152020
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
122019
Harpervalleybank: A domain-specific spoken dialog corpus
M Wu, J Nafziger, A Scodary, A Maas
arXiv preprint arXiv:2010.13929, 2020
92020
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
82021
Generative grading: Neural approximate parsing for automated student feedback
A Malik, M Wu, V Vasavada, J Song, J Mitchell, N Goodman, C Piech
arXiv preprint arXiv:1905.09916, 2019
82019
A simple framework for uncertainty in contrastive learning
M Wu, N Goodman
arXiv preprint arXiv:2010.02038, 2020
62020
Regional tree regularization for interpretability in black box models
M Wu, S Parbhoo, M Hughes, R Kindle, L Celi, M Zazzi, V Roth, ...
arXiv preprint arXiv:1908.04494, 2019
52019
ProtoTransformer: A meta-learning approach to providing student feedback
M Wu, N Goodman, C Piech, C Finn
arXiv preprint arXiv:2107.14035, 2021
42021
Multimodal generative models for compositional representation learning
M Wu, N Goodman
arXiv preprint arXiv:1912.05075, 2019
42019
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