Justin M. Baker

According to our database, Justin M. Baker authored at least 14 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Conformational Rank Conditioned Committees for Machine Learning-Assisted Directed Evolution.

Mia Adler, Carrie Liang, Brian Peng, Oleg Presnyakov, Justin M. Baker, Jannelle Lauffer, Himani Sharma, Barry Merriman

CoRR, October, 2025

Regularized reduced order Lippmann-Schwinger-Lanczos method for inverse scattering problems in the frequency domain.

Justin M. Baker, Elena Cherkaev, Vladimir Druskin, Shari Moskow, Mikhail Zaslavsky

J. Comput. Phys., 2025

Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs.

Shih-Hsin Wang, Yuhao Huang, Taos Transue, Justin M. Baker, Jonathan Forstater, Thomas Strohmer, Bao Wang

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

A Theoretically-Principled Sparse, Connected, and Rigid Graph Representation of Molecules.

Shih-Hsin Wang, Yuhao Huang, Justin M. Baker, Yuan-En Sun, Qi Tang, Bao Wang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Boltzmann Graph Ensemble Embeddings for Aptamer Libraries.

Starlika Bauskar, Jade Jiao, Narayanan Kannan, Alexander Kimm, Justin M. Baker, Matthew J. Tyler, Andrea L. Bertozzi, Anne M. Andrews

Proceedings of the IEEE International Conference on Data Mining, 2025

2024

Learning to Control the Smoothness of Graph Convolutional Network Features.

Shih-Hsin Wang, Justin M. Baker, Cory D. Hauck, Bao Wang

CoRR, 2024

An Explicit Frame Construction for Normalizing 3D Point Clouds.

Justin M. Baker, Shih-Hsin Wang, Tommaso de Fernex, Bao Wang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks.

Shih-Hsin Wang, Yung-Chang Hsu, Justin M. Baker, Andrea L. Bertozzi, Jack Xin, Bao Wang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Monotone Operator Theory-Inspired Message Passing for Learning Long-Range Interaction on Graphs.

Justin M. Baker, Qingsong Wang, Martin Berzins, Thomas Strohmer, Bao Wang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Learning Proper Orthogonal Decomposition of Complex Dynamics Using Heavy-ball Neural ODEs.

Justin M. Baker, Elena Cherkaev, Akil Narayan, Bao Wang

J. Sci. Comput., May, 2023

Regularized Reduced Order Lippman-Schwinger-Lanczos Method for Inverse Scattering Problems in the Frequency Domain.

Justin M. Baker, Elena Cherkaev, Vladimir Druskin, Shari Moskow, Mikhail Zaslavsky

CoRR, 2023

Implicit Graph Neural Networks: A Monotone Operator Viewpoint.

Justin M. Baker, Qingsong Wang, Cory D. Hauck, Bao Wang

Proceedings of the International Conference on Machine Learning, 2023

2022

Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs.

Justin M. Baker, Hedi Xia, Yiwei Wang, Elena Cherkaev, Akil Narayan, Long Chen, Jack Xin, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang

CoRR, 2022

Learning POD of Complex Dynamics Using Heavy-ball Neural ODEs.

Justin M. Baker, Elena Cherkaev, Akil Narayan, Bao Wang

CoRR, 2022