| Discovering Latent Network Structure in Point Process Data SW Linderman, RP Adams Proceedings of The 31st International Conference on Machine Learning, 1413–1421, 2014 | 235 | 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 | 141 | 2018 |
| Variational sequential monte carlo C Naesseth, S Linderman, R Ranganath, D Blei International Conference on Artificial Intelligence and Statistics, 968-977, 2018 | 111 | 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 | 110* | 2017 |
| Reparameterization gradients through acceptance-rejection sampling algorithms C Naesseth, F Ruiz, S Linderman, D Blei Artificial Intelligence and Statistics, 489-498, 2017 | 77 | 2017 |
| Learning latent permutations with gumbel-sinkhorn networks G Mena, D Belanger, S Linderman, J Snoek arXiv preprint arXiv:1802.08665, 2018 | 75 | 2018 |
| Dependent Multinomial Models Made Easy: Stick Breaking with the P\'olya-Gamma Augmentation SW Linderman, MJ Johnson, RP Adams arXiv preprint arXiv:1506.05843, 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 | 41 | 2016 |
| Bayesian latent structure discovery from multi-neuron recordings S Linderman, RP Adams, JW Pillow Advances in Neural Information Processing Systems, 2002-2010, 2016 | 41 | 2016 |
| Scalable bayesian inference for excitatory point process networks SW Linderman, RP Adams arXiv preprint arXiv:1507.03228, 2015 | 38 | 2015 |
| 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 | 36 | 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 | 36* | 2016 |
| 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 | 23 | 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 | 20 | 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 | 20 | 2018 |
| A framework for studying synaptic plasticity with neural spike train data SW Linderman, CH Stock, RP Adams arXiv preprint arXiv:1411.4077, 2014 | 17 | 2014 |
| Using computational theory to constrain statistical models of neural data SW Linderman, SJ Gershman Current opinion in neurobiology 46, 14-24, 2017 | 16 | 2017 |
| Point process latent variable models of larval zebrafish behavior A Sharma, R Johnson, F Engert, S Linderman Advances in Neural Information Processing Systems 31, 10919-10930, 2018 | 14 | 2018 |
| Bayesian inference for latent Hawkes processes SW Linderman, Y Wang, DM Blei Advances in Neural Information Processing Systems, 2017 | 13 | 2017 |
| 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, 15706-15717, 2019 | 11 | 2019 |
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
