Scott Linderman

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

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Scott Linderman

Stanford University

Email yang diverifikasi di stanford.edu - Beranda

Machine Learning Computational Neuroscience


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Simplified state space layers for sequence modeling JTH Smith, A Warrington, SW Linderman The International Conference on Learning Representations, 2022	475	2022
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	421	2018
Discovering Latent Network Structure in Point Process Data SW Linderman, RP Adams Proceedings of The 31st International Conference on Machine Learning, 1413–1421, 2014	350	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	345*	2017
Learning latent permutations with gumbel-sinkhorn networks G Mena, D Belanger, S Linderman, J Snoek arXiv preprint arXiv:1802.08665, 2018	290	2018
Variational sequential monte carlo C Naesseth, S Linderman, R Ranganath, D Blei International conference on artificial intelligence and statistics, 968-977, 2018	268	2018
Reparameterization gradients through acceptance-rejection sampling algorithms C Naesseth, F Ruiz, S Linderman, D Blei Artificial Intelligence and Statistics, 489-498, 2017	139	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	138	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	135	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	125	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	124	2021
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	94	2019
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	93	2019
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	88	2020
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	87	2018
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	76	2024
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	73	2023
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

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