Mike Wu
Cited by
Cited by
Beyond sparsity: Tree regularization of deep models for interpretability
M Wu, MC Hughes, S Parbhoo, M Zazzi, V Roth, F Doshi-Velez
arXiv preprint arXiv:1711.06178, 2017
Multimodal generative models for scalable weakly-supervised learning
M Wu, N Goodman
Advances in Neural Information Processing Systems, 5575-5585, 2018
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
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
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, 782-790, 2019
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
Meta-amortized variational inference and learning
K Choi, M Wu, N Goodman, S Ermon
International Conference on Learning Representations, 2019
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
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
Computing engine, software, system and method
F Wood, M Wu, Y Perov, H Yang
US Patent App. 15/465,131, 2017
Optimizing for interpretability in deep neural networks with tree regularization
M Wu, S Parbhoo, MC Hughes, V Roth, F Doshi-Velez
arXiv preprint arXiv:1908.05254, 2019
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
Beyond sparsity: Tree-based regularization of deep models for interpretability
M Wu, M Hughes, S Parbhoo, F Doshi-Velez
In: Neural Information Processing Systems (NIPS) Conference. Transparent and …, 2017
Spreadsheet probabilistic programming
M Wu, Y Perov, F Wood, H Yang
arXiv preprint arXiv:1606.04216, 2016
Variational Item Response Theory: Fast, Accurate, and Expressive
M Wu, RL Davis, BW Domingue, C Piech, N Goodman
arXiv preprint arXiv:2002.00276, 2020
On the Importance of Views in Unsupervised Representation Learning
M Wu, C Zhuang, D Yamins, N Goodman
preprint 3, 2020
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
Investigating the cosmic web with topological data analysis
J Cisewski-Kehe, M Wu, B Fasy, W Hellwing, M Lovell, A Rinaldo, ...
AAS 231, 213.07, 2018
Topological hypothesis tests for the large-scale structure of the Universe
M Wu, J Cisewski-Kehe, BT Fasy, W Hellwing, MR Lovell, A Rinaldo, ...
preparation, 2018
Financial Market Prediction
M Wu
arXiv preprint arXiv:1503.02328, 2015
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