Publications |
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Jiaxin Zhang, Kyle Saleeby, Thomas Feldhausen, Sirui Bi, Alex Plotkowski, David Womble. Self-Supervised Anomaly Detection via Neural Autoregressive Flows with Active Learning. In NeurIPS 2021 Workshop Deep Generative Models and Downstream Applications. |
Victor Fung, Jiaxin Zhang, Guoxiang Hu, P Ganesh, Bobby G Sumpter. Inverse design of two-dimensional materials with invertible neural networks. In npj Computational Materials - Nature, 2021 (accepted). |
J. Sun, L. Yang, J. Zhang, F. Liu, M. M. Halappanavar, D. Fan, Y. Cao, "Self-supervised Novelty Detection for Continual Learning: A Gradient-based Approach Boosted by Binary Classification", IJCAI, Workshop on Continual Learning |
P. Ramuhalli, V. Chandan, M. Schram, J. Drgona, M. Halappanavar, F. Liu, “Overview of drivers for AI-informed decision and control in energy applications,” Presented at the SIAM AN21 Minisymposium, July 2021. |
F. Liu, M. Halappanavar, Y. Cao and P. Li and D. Womble, "Data-Driven Framework for Decision and Control of Dynamic Systems", Presented at the SIAM AN21 Minisymposium |
Victor Fung, Jiaxin Zhang, Eric Juarez, Bobby Sumpter. Benchmarking graph neural networks for materials chemistry. In npj Computational Materials - Nature, 7, 84, 2021. |
Jiaxin Zhang, Jan Drgona, Sayak Mukherjee, Mahantesh Halappanavar, Frank Liu. Variational Generative Flows for Reconstruction Uncertainty Estimation. In ICML 2021 Workshop on Uncertainty & Robustness in Deep Learning. |
Jiaxin Zhang, Victor Fung. Efficient Inverse Learning for Materials Design and Discovery. In ICLR 2021 Workshop on Science and Engineering of Deep Learning. |
Jan Drgona, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar, "On the Stochastic Stability of Deep Markov Models", NeurIPS 2021 |
D Lu, "Accurate and timely forecasts of geologic carbon storage using machine learning methods", NeurIPS workshops 2021 |