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ORCIDJustin M. Baker
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2020 – today
- 2024
[c4]Justin M. Baker, Qingsong Wang, Martin Berzins, Thomas Strohmer, Bao Wang:
Monotone Operator Theory-Inspired Message Passing for Learning Long-Range Interaction on Graphs. AISTATS 2024: 2233-2241- [c3]Shih-Hsin Wang, Yung-Chang Hsu, Justin M. Baker, Andrea L. Bertozzi, Jack Xin, Bao Wang:
Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks. ICLR 2024 - [c2]Justin M. Baker, Shih-Hsin Wang, Tommaso de Fernex, Bao Wang:
An Explicit Frame Construction for Normalizing 3D Point Clouds. ICML 2024 - 2023
- [j1]Justin M. Baker, Elena Cherkaev, Akil Narayan, Bao Wang:
Learning Proper Orthogonal Decomposition of Complex Dynamics Using Heavy-ball Neural ODEs. J. Sci. Comput. 95(2): 54 (2023) - [c1]Justin M. Baker, Qingsong Wang, Cory D. Hauck, Bao Wang:
Implicit Graph Neural Networks: A Monotone Operator Viewpoint. ICML 2023: 1521-1548 - [i3]Justin M. Baker, Elena Cherkaev, Vladimir Druskin, Shari Moskow, Mikhail Zaslavsky:
Regularized Reduced Order Lippman-Schwinger-Lanczos Method for Inverse Scattering Problems in the Frequency Domain. CoRR abs/2311.16367 (2023) - 2022
- [i2]Justin M. Baker, Elena Cherkaev, Akil Narayan, Bao Wang:
Learning POD of Complex Dynamics Using Heavy-ball Neural ODEs. CoRR abs/2202.12373 (2022) - [i1]Justin M. Baker, Hedi Xia, Yiwei Wang, Elena Cherkaev, Akil Narayan, Long Chen, Jack Xin, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs. CoRR abs/2204.08621 (2022)
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