Scott Linderman
Scott Linderman
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
Discovering Latent Network Structure in Point Process Data
SW Linderman, RP Adams
Proceedings of The 31st International Conference on Machine Learning, 1413–1421, 2014
2462014
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
1672018
Variational sequential monte carlo
C Naesseth, S Linderman, R Ranganath, D Blei
International Conference on Artificial Intelligence and Statistics, 968-977, 2018
1202018
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
115*2017
Learning latent permutations with gumbel-sinkhorn networks
G Mena, D Belanger, S Linderman, J Snoek
arXiv preprint arXiv:1802.08665, 2018
852018
Reparameterization gradients through acceptance-rejection sampling algorithms
C Naesseth, F Ruiz, S Linderman, D Blei
Artificial Intelligence and Statistics, 489-498, 2017
822017
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
712015
Bayesian latent structure discovery from multi-neuron recordings
S Linderman, RP Adams, JW Pillow
Advances in Neural Information Processing Systems, 2002-2010, 2016
442016
Bayesian latent structure discovery from multi-neuron recordings
S Linderman, RP Adams, JW Pillow
Advances in Neural Information Processing Systems, 2002-2010, 2016
442016
Scalable bayesian inference for excitatory point process networks
SW Linderman, RP Adams
arXiv preprint arXiv:1507.03228, 2015
402015
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
392018
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
39*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
322020
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
252019
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
222018
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
192019
Using computational theory to constrain statistical models of neural data
SW Linderman, SJ Gershman
Current opinion in neurobiology 46, 14-24, 2017
192017
A framework for studying synaptic plasticity with neural spike train data
SW Linderman, CH Stock, RP Adams
arXiv preprint arXiv:1411.4077, 2014
182014
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
152018
Bayesian inference for latent Hawkes processes
SW Linderman, Y Wang, DM Blei
Advances in Neural Information Processing Systems, 2017
122017
The system can't perform the operation now. Try again later.
Articles 1–20