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Scott Linderman
Scott Linderman
Stanford University
Подтвержден адрес электронной почты в домене stanford.edu - Главная страница
Название
Процитировано
Процитировано
Год
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
3522018
Discovering Latent Network Structure in Point Process Data
SW Linderman, RP Adams
Proceedings of The 31st International Conference on Machine Learning, 1413–1421, 2014
3372014
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
273*2017
Learning latent permutations with gumbel-sinkhorn networks
G Mena, D Belanger, S Linderman, J Snoek
arXiv preprint arXiv:1802.08665, 2018
2572018
Variational sequential monte carlo
C Naesseth, S Linderman, R Ranganath, D Blei
International conference on artificial intelligence and statistics, 968-977, 2018
2392018
Reparameterization gradients through acceptance-rejection sampling algorithms
C Naesseth, F Ruiz, S Linderman, D Blei
Artificial Intelligence and Statistics, 489-498, 2017
1292017
Simplified state space layers for sequence modeling
JTH Smith, A Warrington, SW Linderman
arXiv preprint arXiv:2208.04933, 2022
1252022
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
1252015
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
1032020
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
762019
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
712018
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
702023
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
702019
Scalable bayesian inference for excitatory point process networks
SW Linderman, RP Adams
arXiv preprint arXiv:1507.03228, 2015
682015
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
662020
Bayesian latent structure discovery from multi-neuron recordings
S Linderman, RP Adams, JW Pillow
Advances in Neural Information Processing Systems, 2002-2010, 2016
652016
Bayesian latent structure discovery from multi-neuron recordings
S Linderman, RP Adams, JW Pillow
Advances in Neural Information Processing Systems, 2002-2010, 2016
652016
Generalized shape metrics on neural representations
AH Williams, E Kunz, S Kornblith, S Linderman
Advances in Neural Information Processing Systems 34, 4738-4750, 2021
592021
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
572018
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
52*2016
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