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