| Simplified state space layers for sequence modeling JTH Smith, A Warrington, SW Linderman The International Conference on Learning Representations, 2022 | 486 | 2022 |
| The striatum organizes 3D behavior via moment-to-moment action selection JE Markowitz, WF Gillis, CC Beron, SQ Neufeld, K Robertson, ND Bhagat, ... Cell 174 (1), 44-58. e17, 2018 | 422 | 2018 |
| Discovering Latent Network Structure in Point Process Data SW Linderman, RP Adams Proceedings of The 31st International Conference on Machine Learning, 1413–1421, 2014 | 350 | 2014 |
| Bayesian learning and inference in recurrent switching linear dynamical systems S Linderman, M Johnson, A Miller, R Adams, D Blei, L Paninski Artificial intelligence and statistics, 914-922, 2017 | 346* | 2017 |
| Learning latent permutations with gumbel-sinkhorn networks G Mena, D Belanger, S Linderman, J Snoek arXiv preprint arXiv:1802.08665, 2018 | 293 | 2018 |
| Variational sequential monte carlo C Naesseth, S Linderman, R Ranganath, D Blei International conference on artificial intelligence and statistics, 968-977, 2018 | 268 | 2018 |
| Reparameterization gradients through acceptance-rejection sampling algorithms C Naesseth, F Ruiz, S Linderman, D Blei Artificial Intelligence and Statistics, 489-498, 2017 | 139 | 2017 |
| Dependent multinomial models made easy: Stick-breaking with the Pólya-Gamma augmentation S Linderman, MJ Johnson, RP Adams Advances in neural information processing systems 28, 2015 | 137 | 2015 |
| Spontaneous behaviour is structured by reinforcement without explicit reward JE Markowitz, WF Gillis, M Jay, J Wood, RW Harris, R Cieszkowski, ... Nature 614 (7946), 108-117, 2023 | 135 | 2023 |
| Generalized shape metrics on neural representations AH Williams, E Kunz, S Kornblith, S Linderman Advances in Neural Information Processing Systems 34, 4738-4750, 2021 | 127 | 2021 |
| Probabilistic models of larval zebrafish behavior reveal structure on many scales RE Johnson, S Linderman, T Panier, CL Wee, E Song, KJ Herrera, ... Current Biology 30 (1), 70-82. e4, 2020 | 125 | 2020 |
| Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C. elegans S Linderman, A Nichols, D Blei, M Zimmer, L Paninski BioRxiv, 621540, 2019 | 94 | 2019 |
| BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos E Batty, M Whiteway, S Saxena, D Biderman, T Abe, S Musall, W Gillis, ... Advances in Neural Information Processing Systems 32, 2019 | 94 | 2019 |
| Recurrent switching dynamical systems models for multiple interacting neural populations J Glaser, M Whiteway, JP Cunningham, L Paninski, S Linderman Advances in neural information processing systems 33, 14867-14878, 2020 | 89 | 2020 |
| Tree-structured recurrent switching linear dynamical systems for multi-scale modeling J Nassar, SW Linderman, M Bugallo, IM Park arXiv preprint arXiv:1811.12386, 2018 | 87 | 2018 |
| Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics C Weinreb, JE Pearl, S Lin, MAM Osman, L Zhang, S Annapragada, ... Nature Methods 21 (7), 1329-1339, 2024 | 79 | 2024 |
| An approximate line attractor in the hypothalamus encodes an aggressive state A Nair, T Karigo, B Yang, S Ganguli, MJ Schnitzer, SW Linderman, ... Cell 186 (1), 178-193. e15, 2023 | 75 | 2023 |
| Scalable bayesian inference for excitatory point process networks SW Linderman, RP Adams arXiv preprint arXiv:1507.03228, 2015 | 71 | 2015 |
| Bayesian latent structure discovery from multi-neuron recordings S Linderman, RP Adams, JW Pillow Advances in Neural Information Processing Systems, 2002-2010, 2016 | 69 | 2016 |
| Bayesian latent structure discovery from multi-neuron recordings S Linderman, RP Adams, JW Pillow Advances in Neural Information Processing Systems, 2002-2010, 2016 | 69 | 2016 |
David BleiProfessor of Statistics and Computer Science, Columbia Universitycolumbia.edu의 이메일 확인됨