| Simplified state space layers for sequence modeling JTH Smith, A Warrington, SW Linderman arXiv preprint arXiv:2208.04933, 2022 | 282 | 2022 |
| 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 | 22 | 2021 |
| 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 | 8 | 2024 |
| 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 | 7 | 2019 |
| State-Free Inference of State-Space Models: The Transfer Function Approach RN Parnichkun, S Massaroli, A Moro, JTH Smith, R Hasani, M Lechner, ... arXiv preprint arXiv:2405.06147, 2024 | 2 | 2024 |
| Towards Scalable and Stable Parallelization of Nonlinear RNNs X Gonzalez, A Warrington, JTH Smith, SW Linderman arXiv preprint arXiv:2407.19115, 2024 | | 2024 |
| Towards a theory of learning dynamics in deep state space models J Smékal, JTH Smith, M Kleinman, D Biderman, SW Linderman arXiv preprint arXiv:2407.07279, 2024 | | 2024 |
| Convolutional structured state space model J Smith, W Byeon, S De Mello US Patent App. 18/452,714, 2024 | | 2024 |
| Bayesian Inference in Augmented Bow Tie Networks JTH Smith, D Lawson, SW Linderman | | |
Scott LindermanStanford UniversityVerified email at stanford.edu
