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Scott Linderman
Scott Linderman
Verified email at stanford.edu - Homepage
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
3032014
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
2592018
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
200*2017
Variational sequential monte carlo
C Naesseth, S Linderman, R Ranganath, D Blei
International conference on artificial intelligence and statistics, 968-977, 2018
1822018
Learning latent permutations with gumbel-sinkhorn networks
G Mena, D Belanger, S Linderman, J Snoek
arXiv preprint arXiv:1802.08665, 2018
1672018
Reparameterization gradients through acceptance-rejection sampling algorithms
C Naesseth, F Ruiz, S Linderman, D Blei
Artificial Intelligence and Statistics, 489-498, 2017
1092017
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
1032015
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
76*2019
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
712020
Scalable bayesian inference for excitatory point process networks
SW Linderman, RP Adams
arXiv preprint arXiv:1507.03228, 2015
582015
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
572019
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
572018
Bayesian latent structure discovery from multi-neuron recordings
S Linderman, RP Adams, JW Pillow
Advances in Neural Information Processing Systems, 2002-2010, 2016
552016
Bayesian latent structure discovery from multi-neuron recordings
S Linderman, RP Adams, JW Pillow
Advances in Neural Information Processing Systems, 2002-2010, 2016
552016
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
462018
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
46*2016
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
312020
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
252020
Using computational theory to constrain statistical models of neural data
SW Linderman, SJ Gershman
Current opinion in neurobiology 46, 14-24, 2017
242017
A framework for studying synaptic plasticity with neural spike train data
S Linderman, CH Stock, RP Adams
Advances in neural information processing systems 27, 2014
242014
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