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Actionable Attribution Maps for Scientific Machine Learning.

S. Liu, B. Kailkhura, J. Zhang, A. Hiszpanski, E. Robertson, D. Loveland, and T. Han. CoRR, (2020)

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Yong-son Jin

Kwang Yong Han

Han Yong Oei

Yong-Seop Han

Yong-Fei Han

Other publications of authors with the same name

MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data.Q. Li, B. Kailkhura, R. Anirudh, J. Zhang, Y. Zhou, Y. Liang, T. Han, and P. Varshney. SIAM J. Math. Data Sci., 3 (4): 1197-1222 (2021)Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design.S. Liu, B. Kailkhura, J. Zhang, A. Hiszpanski, E. Robertson, D. Loveland, and T. Han. CoRR, (2020)Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning.A. Mallick, C. Dwivedi, B. Kailkhura, G. Joshi, and T. Han. CoRR, (2020)Deep Probabilistic Kernels for Sample-Efficient Learning.A. Mallick, C. Dwivedi, B. Kailkhura, G. Joshi, and T. Han. CoRR, (2019)Deep kernels with probabilistic embeddings for small-data learning.A. Mallick, C. Dwivedi, B. Kailkhura, G. Joshi, and T. Han. UAI, volume 161 of Proceedings of Machine Learning Research, page 918-928. AUAI Press, (2021)Actionable Attribution Maps for Scientific Machine Learning.S. Liu, B. Kailkhura, J. Zhang, A. Hiszpanski, E. Robertson, D. Loveland, and T. Han. CoRR, (2020)Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science.H. Yang, A. Lam, T. Han, D. Buttler, W. Hsu, C. Aguirre, M. Torre, D. Christensen, L. Bobadilla, E. Davich and 3 other author(s). OST@ICDAR, page 41-46. IEEE, (2019)Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning.J. Zhang, B. Kailkhura, and T. Han. ICML, volume 119 of Proceedings of Machine Learning Research, page 11117-11128. PMLR, (2020)Automated Identification of Molecular Crystals' Packing Motifs.D. Loveland, B. Kailkhura, P. Karande, A. Hiszpanski, and T. Han. J. Chem. Inf. Model., 60 (12): 6147-6154 (2020)Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning.P. Nguyen, D. Loveland, J. Kim, P. Karande, A. Hiszpanski, and T. Han. J. Chem. Inf. Model., 61 (5): 2147-2158 (2021)

BibSonomy

Disambiguation of "Han, Thomas Yong-Jin"

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Actionable Attribution Maps for Scientific Machine Learning.

Please choose a person to relate this publication to

Yong-son Jin

Kwang Yong Han

Han Yong Oei

Yong-Seop Han

Yong-Fei Han

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Han, Thomas Yong-Jin"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Actionable Attribution Maps for Scientific Machine Learning.

Please choose a person to relate this publication to

Yong-son Jin

Kwang Yong Han

Han Yong Oei

Yong-Seop Han

Yong-Fei Han

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Actionable Attribution Maps for Scientific Machine Learning.