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Jimmy T.H. Smith
Jimmy T.H. Smith
PhD student, Stanford
Verified email at stanford.edu
Title
Cited by
Cited by
Year
Simplified state space layers for sequence modeling
JTH Smith, A Warrington, SW Linderman
arXiv preprint arXiv:2208.04933, 2022
882022
Reverse engineering recurrent neural networks with jacobian switching linear dynamical systems
J Smith, S Linderman, D Sussillo
Advances in Neural Information Processing Systems 34, 16700-16713, 2021
152021
All-action policy gradient methods: A numerical integration approach
B Petit, L Amdahl-Culleton, Y Liu, J Smith, PL Bacon
arXiv preprint arXiv:1910.09093, 2019
52019
Convolutional state space models for long-range spatiotemporal modeling
J Smith, S De Mello, J Kautz, S Linderman, W Byeon
Advances in Neural Information Processing Systems 36, 2024
12024
Bayesian Inference in Augmented Bow Tie Networks
JTH Smith, D Lawson, SW Linderman
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Articles 1–5