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machine learning,
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chemistry,
computational biology,
or physics, or papers in more specific areas including
inference, semi-supervised learning , information retrieval, or
reinforcement learning.
Physics papers
P. Holderrieth, Y. Xu, and T. Jaakkola.
Hamiltonian score matching and generative flows.
In Neural Information Processing Systems (NeurIPS), 2024.
X. Fu, A. S. Rosen, K. Bystrom, R. Wang, A. Musaelian, B. Kozinsky, T. Smidt, and T. Jaakkola.
A recipe for charge density prediction.
In Neural Information Processing Systems (NeurIPS), 2024.
[link]
N. Dehmamy, C. Both, J. Mohapatra, S. Das, and T. Jaakkola.
Neural network reparametrization for accelerated optimization in molecular simulations.
In Neural Information Processing Systems (NeurIPS), 2024.
B. Jing, H. Stärk, T. Jaakkola, and B. Berger.
Generative modeling of molecular dynamics trajectories.
In Neural Information Processing Systems (NeurIPS), 2024.
[link]
X. Fu, T. Xie, A. S. Rosen, T. Jaakkola, and J. A. Smith.
Mofdiff: Coarse-grained diffusion for metal-organic framework design.
In The 12th International Conference on Learning Representations (ICLR), 2024.
[link]
X. Fu, T. Xie, N. J. Rebello, B. Olsen, and T. Jaakkola.
Simulate time-integrated coarse-grained molecular dynamics with multi-scale graph networks.
Transactions on Machine Learning Research (TMLR), 2023.
[link]
X. Fu, Z. Wu, W. Wang, T. Xie, S. Keten, R. Gomez-Bombarelli, and T. Jaakkola.
Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations.
Transactions on Machine Learning Research (TMLR), 2023.
[link]
A. Friberg, T. Jaakkola, and J. Tuovinen.
Electromagnetic gaussian beam beyond the paraxial regime.
IEEE Transactions of Antennas and Propagation, 1992.
A. Vasara, M. Taghizadeh, J. Turunen, Westerholmand E. Noponen J., H. Ichikawa, J. Miller, T. Jaakkola, and S. Kuisma.
Binary surface-relief gratings for array illumination in digital optics.
Applied Optics, 31(17):3320--3336, 1992.