Scott W. Linderman

Publication Activity (10 Years)

Years Active: 2014-2023
Publications (10 Years): 46

Top Topics

Dynamical Systems

Bayesian Nonparametric

Reverse Engineer

Top Venues

Publications

Yixiu Zhao, Scott W. Linderman
Revisiting Structured Variational Autoencoders. ICML (2023)
Jimmy T. H. Smith, Andrew Warrington, Scott W. Linderman
Simplified State Space Layers for Sequence Modeling. ICLR (2023)
Yixiu Zhao, Scott W. Linderman
Revisiting Structured Variational Autoencoders. CoRR (2023)
Jimmy T. H. Smith, Shalini De Mello, Jan Kautz, Scott W. Linderman, Wonmin Byeon
Convolutional State Space Models for Long-Range Spatiotemporal Modeling. NeurIPS (2023)
Hyun Dong Lee, Andrew Warrington, Joshua I. Glaser, Scott W. Linderman
Switching Autoregressive Low-rank Tensor Models. CoRR (2023)
Jimmy T. H. Smith, Shalini De Mello, Jan Kautz, Scott W. Linderman, Wonmin Byeon
Convolutional State Space Models for Long-Range Spatiotemporal Modeling. CoRR (2023)
Dieterich Lawson, Michael Li, Scott W. Linderman
NAS-X: Neural Adaptive Smoothing via Twisting. NeurIPS (2023)
Dieterich Lawson, Michael Li, Scott W. Linderman
NAS-X: Neural Adaptive Smoothing via Twisting. CoRR (2023)
Jay A. Hennig, Sandra A. Romero Pinto, Takahiro Yamaguchi, Scott W. Linderman, Naoshige Uchida, Samuel J. Gershman
Emergence of belief-like representations through reinforcement learning. PLoS Comput. Biol. 19 (9) (2023)
Hyun Dong Lee, Andrew Warrington, Joshua I. Glaser, Scott W. Linderman
Switching Autoregressive Low-rank Tensor Models. NeurIPS (2023)
Julia Costacurta, Lea Duncker, Blue Sheffer, Winthrop Gillis, Caleb Weinreb, Jeffrey E. Markowitz, Sandeep R. Datta, Alex H. Williams, Scott W. Linderman
Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs. NeurIPS (2022)
Yixin Wang, Anthony Degleris, Alex H. Williams, Scott W. Linderman
Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models. CoRR (2022)
Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott W. Linderman
SIXO: Smoothing Inference with Twisted Objectives. NeurIPS (2022)
Jimmy T. H. Smith, Andrew Warrington, Scott W. Linderman
Simplified State Space Layers for Sequence Modeling. CoRR (2022)
Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott W. Linderman
SIXO: Smoothing Inference with Twisted Objectives. CoRR (2022)
Jimmy T. H. Smith, Scott W. Linderman, David Sussillo
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems. CoRR (2021)
Jimmy T. H. Smith, Scott W. Linderman, David Sussillo
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems. NeurIPS (2021)
Xinwei Yu, Matthew S. Creamer, Francesco Randi, Anuj K. Sharma, Scott W. Linderman, Andrew M. Leifer
Fast deep learning correspondence for neuron tracking and identification in C.elegans using synthetic training. CoRR (2021)
Alex H. Williams, Erin Kunz, Simon Kornblith, Scott W. Linderman
Generalized Shape Metrics on Neural Representations. CoRR (2021)
Libby Zhang, Tim Dunn, Jesse Marshall, Bence Olveczky, Scott W. Linderman
Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model. AISTATS (2021)
Alex H. Williams, Erin Kunz, Simon Kornblith, Scott W. Linderman
Generalized Shape Metrics on Neural Representations. NeurIPS (2021)
Arunesh Mittal, Scott W. Linderman, John W. Paisley, Paul Sajda
Bayesian recurrent state space model for rs-fMRI. CoRR (2020)
David M. Zoltowski, Jonathan W. Pillow, Scott W. Linderman
A general recurrent state space framework for modeling neural dynamics during decision-making. ICML (2020)
Joshua I. Glaser, Matthew R. Whiteway, John P. Cunningham, Liam Paninski, Scott W. Linderman
Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations. NeurIPS (2020)
Alex H. Williams, Anthony Degleris, Yixin Wang, Scott W. Linderman
Point process models for sequence detection in high-dimensional neural spike trains. NeurIPS (2020)
Alex H. Williams, Anthony Degleris, Yixin Wang, Scott W. Linderman
Point process models for sequence detection in high-dimensional neural spike trains. CoRR (2020)
Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach
Poisson-Randomized Gamma Dynamical Systems. NeurIPS (2019)
Josue Nassar, Scott W. Linderman, Mónica F. Bugallo, Il Memming Park
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling. ICLR (Poster) (2019)
Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach
Poisson-Randomized Gamma Dynamical Systems. CoRR (2019)
Ifigeneia Apostolopoulou, Scott W. Linderman, Kyle Miller, Artur Dubrawski
Mutually Regressive Point Processes. NeurIPS (2019)
Eleanor Batty, Matthew R. Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott W. Linderman, Liam Paninski
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. NeurIPS (2019)
Ruoxi Sun, Scott W. Linderman, Ian Kinsella, Liam Paninski
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models. NeurIPS (2019)
Anuj Sharma, Robert Johnson, Florian Engert, Scott W. Linderman
Point process latent variable models of larval zebrafish behavior. NeurIPS (2018)
Gonzalo E. Mena, David Belanger, Scott W. Linderman, Jasper Snoek
Learning Latent Permutations with Gumbel-Sinkhorn Networks. CoRR (2018)
Josue Nassar, Scott W. Linderman, Mónica F. Bugallo, Il Memming Park
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling. CoRR (2018)
Josue Nassar, Scott W. Linderman, Yuan Zhao, Mónica F. Bugallo, Il Memming Park
Learning Structured Neural Dynamics From Single Trial Population Recording. ACSSC (2018)
Gonzalo E. Mena, David Belanger, Scott W. Linderman, Jasper Snoek
Learning Latent Permutations with Gumbel-Sinkhorn Networks. ICLR (Poster) (2018)
Scott W. Linderman, Gonzalo E. Mena, Hal James Cooper, Liam Paninski, John P. Cunningham
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. AISTATS (2018)
Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei
Variational Sequential Monte Carlo. AISTATS (2018)
Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski
Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. AISTATS (2017)
Scott W. Linderman, Matthew J. Johnson
Structure-Exploiting variational inference for recurrent switching linear dynamical systems. CAMSAP (2017)
Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, David M. Blei
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms. AISTATS (2017)
Huseyin Melih Elibol, Vincent Nguyen, Scott W. Linderman, Matthew J. Johnson, Amna Hashmi, Finale Doshi-Velez
Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders. J. Mach. Learn. Res. 17 (2016)
Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow
Bayesian latent structure discovery from multi-neuron recordings. NIPS (2016)
Zhe Chen, Scott W. Linderman, Matthew A. Wilson
Bayesian nonparametric methods for discovering latent structures of rat hippocampal ensemble spikes. MLSP (2016)
Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation. NIPS (2015)
Scott W. Linderman, Ryan P. Adams
Discovering Latent Network Structure in Point Process Data. ICML (2014)
Scott W. Linderman, Ryan P. Adams
Discovering Latent Network Structure in Point Process Data. CoRR (2014)
Scott W. Linderman, Christopher H. Stock, Ryan P. Adams
A framework for studying synaptic plasticity with neural spike train data. NIPS (2014)