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Locally Constant Networks
We show how neural models can be used to realize piece-wise constant fun...
09/30/2019 ∙ by Guang-He Lee, et al. ∙
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Towards Robust, Locally Linear Deep Networks
Deep networks realize complex mappings that are often understood by thei...
07/07/2019 ∙ by Guang-He Lee, et al. ∙
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A Stratified Approach to Robustness for Randomly Smoothed Classifiers
Strong theoretical guarantees of robustness can be given for ensembles o...
06/12/2019 ∙ by Guang-He Lee, et al. ∙
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Functional Transparency for Structured Data: a Game-Theoretic Approach
We provide a new approach to training neural models to exhibit transpare...
02/26/2019 ∙ by Guang-He Lee, et al. ∙
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Game-Theoretic Interpretability for Temporal Modeling
Interpretability has arisen as a key desideratum of machine learning mod...
06/30/2018 ∙ by Guang-He Lee, et al. ∙
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MUSE: Modularizing Unsupervised Sense Embeddings
This paper proposes to address the word sense ambiguity issue in an unsu...
04/15/2017 ∙ by Guang-He Lee, et al. ∙
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Toward Implicit Sample Noise Modeling: Deviation-driven Matrix Factorization
The objective function of a matrix factorization model usually aims to m...
10/28/2016 ∙ by Guang-He Lee, et al. ∙
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