引用次数
引用: 3,373
H-指数: 29
I10-指数 : 39
出版物: 83
Cross-corpora unsupervised learning of trajectories in autism spectrum disorders
Huseyin Melih Elibol , Vincent Nguyen , Scott Linderman , Matthew Johnson
Journal of Machine Learning Research
1
2016
Using computational theory to constrain statistical models of neural data.
Scott W Linderman , Samuel J Gershman
Current Opinion in Neurobiology 46 14 -24
13
2017
Imaging whole-brain activity to understand behaviour
Albert Lin , Daniel Witvliet , Luis Hernandez-Nunez , Scott W Linderman
Nature Reviews Physics 4 ( 5) 292 -305
10
2022
Simplified state space layers for sequence modeling
Jimmy TH Smith , Andrew Warrington , Scott W Linderman
arXiv preprint arXiv:2208.04933
8
2022
Spontaneous behaviour is structured by reinforcement without explicit reward
Jeffrey E Markowitz , Winthrop F Gillis , Maya Jay , Jeffrey Wood
Nature 614 ( 7946) 108 -117
1
2023
Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training
Xinwei Yu , Matthew S Creamer , Francesco Randi , Anuj K Sharma
Elife 10 e66410
11
2021
Dose–response modeling in high-throughput cancer drug screenings: an end-to-end approach
Wesley Tansey , Kathy Li , Haoran Zhang , Scott W Linderman
Biostatistics 23 ( 2) 643 -665
14
2022
Dose-response modeling in high-throughput cancer drug screenings: a case study with recommendations for practitioners
Wesley Tansey , Kathy Li , Haoran Zhang , Scott W Linderman
arXiv preprint arXiv:1812.05691
2
2018
Rethinking dose-response modeling in high throughput cancer drug screenings: a case study and recommendations for practitioners
Wesley Tansey , Kathy Li , Haoran Zhang , Scott W Linderman
Quantifying the behavioral dynamics of C. elegans with autoregressive hidden Markov models
E Kelly Buchanan , Akiva Lipschitz , Scott W Linderman , Liam Paninski
Workshop on Worm’s neural information processing at the 31st conference on neural information processing systems
8
2017
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference: Supplementary Material
Scott W Linderman , Gonzalo E Mena , Hal Cooper , Liam Paninski
Bayesian inference for latent Hawkes processes
Scott W Linderman , Yixin Wang , David M Blei
Advances in Neural Information Processing Systems
26
2017
Appendix for Poisson-Randomized Gamma Dynamical Systems
Aaron Schein , Scott W Linderman , Mingyuan Zhou , David M Blei
Dynamic and reversible remapping of network representations in an unchanging environment
Isabel IC Low , Alex H Williams , Malcolm G Campbell , Scott W Linderman
Neuron 109 ( 18) 2967 -2980. e11
31
2021
Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics
Caleb Weinreb , Jonah Pearl , Sherry Lin , Mohammed Abdal Monium Osman
BioRxiv
28
2023
Scalable bayesian inference for excitatory point process networks
Scott W Linderman , Ryan P Adams
arXiv preprint arXiv:1507.03228
69
2015
Inferring structured connectivity from spike trains under negative-binomial generalized linear models
Scott W Linderman , Ryan P Adams , Jonathan W Pillow
Neuron 50 ( 100) 150 -150
4
2015
Discovering switching autoregressive dynamics in neural spike train recordings
Scott W Linderman , Matthew J Johnson , Sandeep R Datta , Ryan P Adams
Computational and systems neuroscience (Cosyne)
1
2015
Discovering structure in spiking data
Scott W Linderman , Ryan P Adams
New England Machine Learning Day, Cambridge, MA USA
1
2013
Inferring functional connectivity with priors on network topology
Scott W Linderman , Ryan P Adams
Cosyne Abstracts
1
2013
Ryan P. Adams David Blei Liam Paninski Matthew J. Johnson Sandeep Robert Datta Jeffrey Evan Markowitz BERNARDO SABATINI Alex H Williams Andrew C. Miller Gonzalo Mena John Cunningham Christian A. Naesseth Francisco J. R. Ruiz Jonathan Pillow Zhe Sage Chen Rajesh Ranganath Samuel Gershman Finale Doshi-Velez Shamim Nemati Raffaello Camoriano