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Scott Linderman
Scott Linderman
Verified email at stanford.edu - Homepage
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Cited by
Cited by
Year
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
4092018
Simplified state space layers for sequence modeling
JTH Smith, A Warrington, SW Linderman
The International Conference on Learning Representations, 2022
3872022
Discovering Latent Network Structure in Point Process Data
SW Linderman, RP Adams
Proceedings of The 31st International Conference on Machine Learning, 1413–1421, 2014
3462014
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
331*2017
Learning latent permutations with gumbel-sinkhorn networks
G Mena, D Belanger, S Linderman, J Snoek
arXiv preprint arXiv:1802.08665, 2018
2812018
Variational sequential monte carlo
C Naesseth, S Linderman, R Ranganath, D Blei
International conference on artificial intelligence and statistics, 968-977, 2018
2602018
Reparameterization gradients through acceptance-rejection sampling algorithms
C Naesseth, F Ruiz, S Linderman, D Blei
Artificial Intelligence and Statistics, 489-498, 2017
1362017
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
1362015
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
1232023
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
1192020
Generalized shape metrics on neural representations
AH Williams, E Kunz, S Kornblith, S Linderman
Advances in Neural Information Processing Systems 34, 4738-4750, 2021
1102021
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
902019
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
872019
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
832018
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
812020
Scalable bayesian inference for excitatory point process networks
SW Linderman, RP Adams
arXiv preprint arXiv:1507.03228, 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
692016
Bayesian latent structure discovery from multi-neuron recordings
S Linderman, RP Adams, JW Pillow
Advances in Neural Information Processing Systems, 2002-2010, 2016
692016
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
652023
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
642024
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