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2020 – today
- 2021
[j4]Phan Nguyen
, Donald Loveland, Joanne Taery 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): 2147-2158 (2021)- [j3]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): 1197-1222 (2021) - [c3]Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han:
Deep kernels with probabilistic embeddings for small-data learning. UAI 2021: 918-928 - 2020
- [j2]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): 2876-2887 (2020) - [j1]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): 6147-6154 (2020) - [c2]Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han:
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning. ICML 2020: 11117-11128 - [i7]Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han:
Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning. CoRR abs/2003.07329 (2020) - [i6]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 abs/2006.16533 (2020) - [i5]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 abs/2007.08631 (2020) - [i4]Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han:
Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning. CoRR abs/2007.10800 (2020) - [i3]Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han:
Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows. CoRR abs/2012.01478 (2020)
2010 – 2019
- 2019
- [c1]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: 41-46 - [i2]Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han:
Deep Probabilistic Kernels for Sample-Efficient Learning. CoRR abs/1910.05858 (2019) - [i1]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 abs/1912.07747 (2019)
Coauthor Index
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