Scott Linderman

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Citations	4109	3694
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David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Liam PaninskiColumbia UniversityVerified email at stat.columbia.edu
Sandeep Robert DattaProfessor of Neurobiology, Harvard UniversityVerified email at hms.harvard.edu
Matthew J. JohnsonGoogle BrainVerified email at csail.mit.edu
BERNARDO SABATINIPROFESSOR OF NEUROBIOLOGY, HHMI, HARVARD MEDICAL SCHOOLVerified email at HMS.HARVARD.EDU
Jeffrey Evan MarkowitzAssistant Professor, BME, Georgia Tech and EmoryVerified email at bme.gatech.edu
Alex H WilliamsNew York University & Flatiron InstituteVerified email at nyu.edu
Gonzalo MenaCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Andrew C. MillerResearch Scientist, AppleVerified email at apple.com
John CunninghamProfessor, Columbia UniversityVerified email at columbia.edu
Christian A. NaessethAssistant Professor, University of AmsterdamVerified email at uva.nl
Francisco J. R. RuizResearch Scientist at DeepmindVerified email at google.com
Jonathan PillowProfessor of Neuroscience, Princeton UniversityVerified email at princeton.edu
Zhe Sage ChenProfessor of Psychiatry, Neurosci & Physiology, Biomedical Engineering, New York UniversityVerified email at nyulangone.org
Rajesh RanganathAssistant Professor, NYUVerified email at cs.princeton.edu
Samuel GershmanProfessor, Harvard UniversityVerified email at fas.harvard.edu
Finale Doshi-VelezProfessor, HarvardVerified email at seas.harvard.edu
Shamim NematiUCSDVerified email at ucsd.edu
Raffaello CamorianoAssistant Professor, Politecnico di Torino; Affiliated Researcher, Istituto Italiano di TecnologiaVerified email at polito.it

Scott Linderman

Stanford University

Verified email at stanford.edu - Homepage

Machine Learning Computational Neuroscience


Title Sort by citations Sort by year Sort by title	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	409	2018
Simplified state space layers for sequence modeling JTH Smith, A Warrington, SW Linderman The International Conference on Learning Representations, 2022	389	2022
Discovering Latent Network Structure in Point Process Data SW Linderman, RP Adams Proceedings of The 31st International Conference on Machine Learning, 1413–1421, 2014	346	2014
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	281	2018
Variational sequential monte carlo C Naesseth, S Linderman, R Ranganath, D Blei International conference on artificial intelligence and statistics, 968-977, 2018	260	2018
Reparameterization gradients through acceptance-rejection sampling algorithms C Naesseth, F Ruiz, S Linderman, D Blei Artificial Intelligence and Statistics, 489-498, 2017	136	2017
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	136	2015
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	123	2023
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	120	2020
Generalized shape metrics on neural representations AH Williams, E Kunz, S Kornblith, S Linderman Advances in Neural Information Processing Systems 34, 4738-4750, 2021	110	2021
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	90	2019
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	88	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	83	2018
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	81	2020
Scalable bayesian inference for excitatory point process networks SW Linderman, RP Adams arXiv preprint arXiv:1507.03228, 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	69	2016
Bayesian latent structure discovery from multi-neuron recordings S Linderman, RP Adams, JW Pillow Advances in Neural Information Processing Systems, 2002-2010, 2016	69	2016
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	65	2023
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	64	2024

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