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Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning

Jul 18, 2020
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, T. Yong-Jin Han


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Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design

Jul 16, 2020
Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, T. Yong-Jin Han


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Actionable Attribution Maps for Scientific Machine Learning

Jun 30, 2020
Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, T. Yong-Jin Han


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Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning

Mar 16, 2020
Jize Zhang, Bhavya Kailkhura, T. Yong-Jin Han


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Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science

Dec 16, 2019
Huichen Yang, Carlos A. Aguirre, Maria F. De La Torre, Derek Christensen, Luis Bobadilla, Emily Davich, Jordan Roth, Lei Luo, Yihong Theis, Alice Lam, T. Yong-Jin Han, David Buttler, William H. Hsu

* 15th International Conference on Document Analysis and Recognition Workshops (ICDARW 2019) 

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Deep Probabilistic Kernels for Sample-Efficient Learning

Oct 13, 2019
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, T. Yong-Jin Han


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Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery

Jan 05, 2019
Bhavya Kailkhura, Brian Gallagher, Sookyung Kim, Anna Hiszpanski, T. Yong-Jin Han


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