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Scott Linderman
Scott Linderman
Stanford University
Verificeret mail på stanford.edu - Startside
Titel
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Simplified state space layers for sequence modeling
JTH Smith, A Warrington, SW Linderman
The International Conference on Learning Representations, 2022
5372022
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
4282018
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
353*2017
Discovering Latent Network Structure in Point Process Data
SW Linderman, RP Adams
Proceedings of The 31st International Conference on Machine Learning, 1413–1421, 2014
3532014
Learning latent permutations with gumbel-sinkhorn networks
G Mena, D Belanger, S Linderman, J Snoek
arXiv preprint arXiv:1802.08665, 2018
2992018
Variational sequential monte carlo
C Naesseth, S Linderman, R Ranganath, D Blei
International conference on artificial intelligence and statistics, 968-977, 2018
2702018
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
1462023
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
1412015
Reparameterization gradients through acceptance-rejection sampling algorithms
C Naesseth, F Ruiz, S Linderman, D Blei
Artificial Intelligence and Statistics, 489-498, 2017
1392017
Generalized shape metrics on neural representations
AH Williams, E Kunz, S Kornblith, S Linderman
Advances in Neural Information Processing Systems 34, 4738-4750, 2021
1342021
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
1262020
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
962019
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
952019
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
932020
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
912024
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
872018
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
802023
Scalable bayesian inference for excitatory point process networks
SW Linderman, RP Adams
arXiv preprint arXiv:1507.03228, 2015
742015
Bayesian latent structure discovery from multi-neuron recordings
S Linderman, RP Adams, JW Pillow
Advances in Neural Information Processing Systems, 2002-2010, 2016
712016
Bayesian latent structure discovery from multi-neuron recordings
S Linderman, RP Adams, JW Pillow
Advances in Neural Information Processing Systems, 2002-2010, 2016
712016
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Artikler 1–20