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Scott Linderman
Scott Linderman
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Title
Cited by
Cited by
Year
Discovering Latent Network Structure in Point Process Data
SW Linderman, RP Adams
Proceedings of The 31st International Conference on Machine Learning, 1413–1421, 2014
2882014
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
2342018
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
183*2017
Variational sequential monte carlo
C Naesseth, S Linderman, R Ranganath, D Blei
International conference on artificial intelligence and statistics, 968-977, 2018
1682018
Learning latent permutations with gumbel-sinkhorn networks
G Mena, D Belanger, S Linderman, J Snoek
arXiv preprint arXiv:1802.08665, 2018
1482018
Reparameterization gradients through acceptance-rejection sampling algorithms
C Naesseth, F Ruiz, S Linderman, D Blei
Artificial Intelligence and Statistics, 489-498, 2017
1032017
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
912015
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
652020
Scalable bayesian inference for excitatory point process networks
SW Linderman, RP Adams
arXiv preprint arXiv:1507.03228, 2015
562015
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
542019
Bayesian latent structure discovery from multi-neuron recordings
S Linderman, RP Adams, JW Pillow
Advances in Neural Information Processing Systems, 2002-2010, 2016
522016
Bayesian latent structure discovery from multi-neuron recordings
S Linderman, RP Adams, JW Pillow
Advances in Neural Information Processing Systems, 2002-2010, 2016
522016
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
502018
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
45*2016
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
442019
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
432018
Advances in neural information processing systems
E Batty, M Whiteway, S Saxena, D Biderman, T Abe, S Musall, W Gillis, ...
Curran Associates, Inc 32, 15706-15717, 2019
272019
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
252020
Using computational theory to constrain statistical models of neural data
SW Linderman, SJ Gershman
Current opinion in neurobiology 46, 14-24, 2017
242017
Point process latent variable models of larval zebrafish behavior
A Sharma, R Johnson, F Engert, S Linderman
Advances in Neural Information Processing Systems 31, 2018
212018
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