Scott Linderman

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	Toate	Din 2018
Referințe bibliografice	2664	2476
h-index	23	23
i10-index	37	36

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Ryan P. AdamsPrinceton UniversityAdresă de e-mail confirmată pe princeton.edu
David BleiProfessor of Statistics and Computer Science, Columbia UniversityAdresă de e-mail confirmată pe columbia.edu
Liam PaninskiColumbia UniversityAdresă de e-mail confirmată pe stat.columbia.edu
Matthew J. JohnsonGoogle BrainAdresă de e-mail confirmată pe csail.mit.edu
Sandeep Robert DattaProfessor of Neurobiology, Harvard UniversityAdresă de e-mail confirmată pe hms.harvard.edu
Jeffrey Evan MarkowitzAssistant Professor, BME, Georgia Tech and EmoryAdresă de e-mail confirmată pe bme.gatech.edu
BERNARDO SABATINIPROFESSOR OF NEUROBIOLOGY, HHMI, HARVARD MEDICAL SCHOOLAdresă de e-mail confirmată pe HMS.HARVARD.EDU
Andrew C. MillerResearch Scientist, AppleAdresă de e-mail confirmată pe apple.com
Gonzalo MenaCarnegie Mellon UniversityAdresă de e-mail confirmată pe andrew.cmu.edu
Alex H WilliamsNew York University & Flatiron InstituteAdresă de e-mail confirmată pe nyu.edu
John CunninghamProfessor, Columbia UniversityAdresă de e-mail confirmată pe columbia.edu
Christian A. NaessethAssistant Professor at University of AmsterdamAdresă de e-mail confirmată pe uva.nl
Francisco J. R. RuizResearch Scientist at DeepmindAdresă de e-mail confirmată pe google.com
Jonathan PillowProfessor of Neuroscience, Princeton UniversityAdresă de e-mail confirmată pe princeton.edu
Zhe (Sage) ChenAssociate Professor, School of Medicine, New York UniversityAdresă de e-mail confirmată pe nyulangone.org
Rajesh RanganathAssistant Professor, NYUAdresă de e-mail confirmată pe cs.princeton.edu
Samuel GershmanProfessor, Harvard UniversityAdresă de e-mail confirmată pe fas.harvard.edu
Finale Doshi-VelezProfessor, HarvardAdresă de e-mail confirmată pe seas.harvard.edu
Shamim NematiUCSDAdresă de e-mail confirmată pe ucsd.edu
Raffaello CamorianoAssistant Professor (non-TT, RTDa), Politecnico di TorinoAdresă de e-mail confirmată pe polito.it

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Scott Linderman

Stanford University

Adresă de e-mail confirmată pe stanford.edu - Pagina de pornire

Machine Learning Computational Neuroscience


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Discovering Latent Network Structure in Point Process Data SW Linderman, RP Adams Proceedings of The 31st International Conference on Machine Learning, 1413–1421, 2014	327	2014
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	319	2018
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	235*	2017
Learning latent permutations with gumbel-sinkhorn networks G Mena, D Belanger, S Linderman, J Snoek arXiv preprint arXiv:1802.08665, 2018	219	2018
Variational sequential monte carlo C Naesseth, S Linderman, R Ranganath, D Blei International conference on artificial intelligence and statistics, 968-977, 2018	218	2018
Reparameterization gradients through acceptance-rejection sampling algorithms C Naesseth, F Ruiz, S Linderman, D Blei Artificial Intelligence and Statistics, 489-498, 2017	121	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	119	2015
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	85	2020
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	71	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	65	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	65	2018
Scalable bayesian inference for excitatory point process networks SW Linderman, RP Adams arXiv preprint arXiv:1507.03228, 2015	65	2015
Bayesian latent structure discovery from multi-neuron recordings S Linderman, RP Adams, JW Pillow Advances in Neural Information Processing Systems, 2002-2010, 2016	60	2016
Bayesian latent structure discovery from multi-neuron recordings S Linderman, RP Adams, JW Pillow Advances in Neural Information Processing Systems, 2002-2010, 2016	60	2016
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	53	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	52	2020
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	49*	2016
Simplified state space layers for sequence modeling JTH Smith, A Warrington, SW Linderman arXiv preprint arXiv:2208.04933, 2022	38	2022
Generalized shape metrics on neural representations AH Williams, E Kunz, S Kornblith, S Linderman Advances in Neural Information Processing Systems 34, 4738-4750, 2021	33	2021
A general recurrent state space framework for modeling neural dynamics during decision-making D Zoltowski, J Pillow, S Linderman International Conference on Machine Learning, 11680-11691, 2020	30	2020

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