| Discovering Latent Network Structure in Point Process Data SW Linderman, RP Adams Proceedings of The 31st International Conference on Machine Learning, 1413–1421, 2014 | 303 | 2014 |
| 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 | 259 | 2018 |
| 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 | 200* | 2017 |
| Variational sequential monte carlo C Naesseth, S Linderman, R Ranganath, D Blei International conference on artificial intelligence and statistics, 968-977, 2018 | 182 | 2018 |
| Learning latent permutations with gumbel-sinkhorn networks G Mena, D Belanger, S Linderman, J Snoek arXiv preprint arXiv:1802.08665, 2018 | 167 | 2018 |
| Reparameterization gradients through acceptance-rejection sampling algorithms C Naesseth, F Ruiz, S Linderman, D Blei Artificial Intelligence and Statistics, 489-498, 2017 | 109 | 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 | 103 | 2015 |
| 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 | 76* | 2019 |
| 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 | 71 | 2020 |
| Scalable bayesian inference for excitatory point process networks SW Linderman, RP Adams arXiv preprint arXiv:1507.03228, 2015 | 58 | 2015 |
| 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 | 57 | 2019 |
| 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 | 57 | 2018 |
| Bayesian latent structure discovery from multi-neuron recordings S Linderman, RP Adams, JW Pillow Advances in Neural Information Processing Systems, 2002-2010, 2016 | 55 | 2016 |
| Bayesian latent structure discovery from multi-neuron recordings S Linderman, RP Adams, JW Pillow Advances in Neural Information Processing Systems, 2002-2010, 2016 | 55 | 2016 |
| Reparameterizing the birkhoff polytope for variational permutation inference S Linderman, G Mena, H Cooper, L Paninski, J Cunningham International Conference on Artificial Intelligence and Statistics, 1618-1627, 2018 | 46 | 2018 |
| A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation SW Linderman, MJ Johnson, MA Wilson, Z Chen Journal of neuroscience methods 263, 36-47, 2016 | 46* | 2016 |
| 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 | 31 | 2020 |
| A general recurrent state space framework for modeling neural dynamics during decision-making D Zoltowski, J Pillow, S Linderman International Conference on Machine Learning, 11680-11691, 2020 | 25 | 2020 |
| Using computational theory to constrain statistical models of neural data SW Linderman, SJ Gershman Current opinion in neurobiology 46, 14-24, 2017 | 24 | 2017 |
| A framework for studying synaptic plasticity with neural spike train data S Linderman, CH Stock, RP Adams Advances in neural information processing systems 27, 2014 | 24 | 2014 |
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
