Thomas Yong-Jin Han

Publication Activity (10 Years)

Years Active: 2019-2021
Publications (10 Years): 14

Top Topics

Relational Models

Scientific Articles

Top Venues

J. Chem. Inf. Model.

SIAM J. Math. Data Sci.

Publications

Phan Nguyen, Donald Loveland, Joanne T. Kim, Piyush Karande, Anna M. Hiszpanski, Thomas Yong-Jin Han
Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning. J. Chem. Inf. Model. 61 (5) (2021)
Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Jize Zhang, Yi Zhou, Yingbin Liang, Thomas Yong-Jin Han, Pramod K. Varshney
MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data. SIAM J. Math. Data Sci. 3 (4) (2021)
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han
Deep kernels with probabilistic embeddings for small-data learning. UAI (2021)
Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han
Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design. CoRR (2020)
Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han
Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows. CoRR (2020)
Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning. ICML (2020)
Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han
Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning. CoRR (2020)
Anna M. Hiszpanski, Brian Gallagher, Karthik Chellappan, Peggy Li, Shusen Liu, Hyojin Kim, Jinkyu Han, Bhavya Kailkhura, David J. Buttler, Thomas Yong-Jin Han
Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge. J. Chem. Inf. Model. 60 (6) (2020)
Donald Loveland, Bhavya Kailkhura, Piyush Karande, Anna M. Hiszpanski, Thomas Yong-Jin Han
Automated Identification of Molecular Crystals' Packing Motifs. J. Chem. Inf. Model. 60 (12) (2020)
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han
Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning. CoRR (2020)
Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han
Actionable Attribution Maps for Scientific Machine Learning. CoRR (2020)
Huichen Yang, Carlos A. Aguirre, Maria F. De La Torre, Derek Christensen, Luis Bobadilla, Emily Davich, Jordan Roth, Lei Luo, Yihong Theis, Alice Lam, Thomas Yong-Jin Han, David Buttler, William H. Hsu
Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science. CoRR (2019)
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han
Deep Probabilistic Kernels for Sample-Efficient Learning. CoRR (2019)
Huichen Yang, Alice Lam, Thomas Yong-Jin Han, David Buttler, William H. Hsu, Carlos A. Aguirre, Maria F. De La Torre, Derek Christensen, Luis Bobadilla, Emily Davich, Jordan Roth, Lei Luo, Yihong Theis
Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science. OST@ICDAR (2019)