Scott W. Linderman

According to our database, Scott W. Linderman authored at least 40 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems.

Amber Hu, David M. Zoltowski, Aditya Nair, David Anderson, Lea Duncker, Scott W. Linderman

CoRR, 2024

Informed Correctors for Discrete Diffusion Models.

Yixiu Zhao, Jiaxin Shi, Lester Mackey, Scott W. Linderman

CoRR, 2024

Towards Scalable and Stable Parallelization of Nonlinear RNNs.

Xavier Gonzalez, Andrew Warrington, Jimmy T. H. Smith, Scott W. Linderman

CoRR, 2024

Towards a theory of learning dynamics in deep state space models.

Jakub Smékal, Jimmy T. H. Smith, Michael Kleinman, Dan Biderman, Scott W. Linderman

CoRR, 2024

2023

Emergence of belief-like representations through reinforcement learning.

Jay A. Hennig, Sandra A. Romero Pinto, Takahiro Yamaguchi, Scott W. Linderman, Naoshige Uchida, Samuel J. Gershman

PLoS Comput. Biol., 2023

Convolutional State Space Models for Long-Range Spatiotemporal Modeling.

Jimmy T. H. Smith, Shalini De Mello, Jan Kautz, Scott W. Linderman, Wonmin Byeon

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Switching Autoregressive Low-rank Tensor Models.

Hyun Dong Lee, Andrew Warrington, Joshua I. Glaser, Scott W. Linderman

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

NAS-X: Neural Adaptive Smoothing via Twisting.

Dieterich Lawson, Michael Li, Scott W. Linderman

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Revisiting Structured Variational Autoencoders.

Yixiu Zhao, Scott W. Linderman

Proceedings of the International Conference on Machine Learning, 2023

Simplified State Space Layers for Sequence Modeling.

Jimmy T. H. Smith, Andrew Warrington, Scott W. Linderman

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models.

Yixin Wang, Anthony Degleris, Alex H. Williams, Scott W. Linderman

CoRR, 2022

SIXO: Smoothing Inference with Twisted Objectives.

Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott W. Linderman

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs.

Julia Costacurta, Lea Duncker, Blue Sheffer, Winthrop Gillis, Caleb Weinreb, Jeffrey E. Markowitz, Sandeep R. Datta, Alex H. Williams, Scott W. Linderman

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Fast deep learning correspondence for neuron tracking and identification in C.elegans using synthetic training.

Xinwei Yu, Matthew S. Creamer, Francesco Randi, Anuj K. Sharma, Scott W. Linderman, Andrew M. Leifer

CoRR, 2021

Generalized Shape Metrics on Neural Representations.

Alex H. Williams, Erin Kunz, Simon Kornblith, Scott W. Linderman

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems.

Jimmy T. H. Smith, Scott W. Linderman, David Sussillo

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model.

Libby Zhang, Tim Dunn, Jesse Marshall, Bence Olveczky, Scott W. Linderman

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Bayesian recurrent state space model for rs-fMRI.

Arunesh Mittal, Scott W. Linderman, John W. Paisley, Paul Sajda

CoRR, 2020

Point process models for sequence detection in high-dimensional neural spike trains.

Alex H. Williams, Anthony Degleris, Yixin Wang, Scott W. Linderman

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations.

Joshua I. Glaser, Matthew R. Whiteway, John P. Cunningham, Liam Paninski, Scott W. Linderman

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A general recurrent state space framework for modeling neural dynamics during decision-making.

David M. Zoltowski, Jonathan W. Pillow, Scott W. Linderman

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models.

Ruoxi Sun, Scott W. Linderman, Ian Kinsella, Liam Paninski

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Poisson-Randomized Gamma Dynamical Systems.

Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos.

Eleanor Batty, Matthew R. Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey E. Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott W. Linderman, Liam Paninski

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Mutually Regressive Point Processes.

Ifigeneia Apostolopoulou, Scott W. Linderman, Kyle Miller, Artur Dubrawski

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling.

Josue Nassar, Scott W. Linderman, Mónica F. Bugallo, Il Memming Park

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Point process latent variable models of larval zebrafish behavior.

Anuj Sharma, Robert Johnson, Florian Engert, Scott W. Linderman

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning Latent Permutations with Gumbel-Sinkhorn Networks.

Gonzalo E. Mena, David Belanger, Scott W. Linderman, Jasper Snoek

Proceedings of the 6th International Conference on Learning Representations, 2018

Variational Sequential Monte Carlo.

Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Reparameterizing the Birkhoff Polytope for Variational Permutation Inference.

Scott W. Linderman, Gonzalo E. Mena, Hal James Cooper, Liam Paninski, John P. Cunningham

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Learning Structured Neural Dynamics From Single Trial Population Recording.

Josue Nassar, Scott W. Linderman, Yuan Zhao, Mónica F. Bugallo, Il Memming Park

Proceedings of the 52nd Asilomar Conference on Signals, Systems, and Computers, 2018

2017

Structure-Exploiting variational inference for recurrent switching linear dynamical systems.

Scott W. Linderman, Matthew J. Johnson

Proceedings of the 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2017

Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms.

Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, David M. Blei

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems.

Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders.

Huseyin Melih Elibol, Vincent Nguyen, Scott W. Linderman, Matthew J. Johnson, Amna Hashmi, Finale Doshi-Velez

J. Mach. Learn. Res., 2016

Bayesian latent structure discovery from multi-neuron recordings.

Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Bayesian nonparametric methods for discovering latent structures of rat hippocampal ensemble spikes.

Zhe Chen, Scott W. Linderman, Matthew A. Wilson

Proceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing, 2016

2015

Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation.

Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

A framework for studying synaptic plasticity with neural spike train data.

Scott W. Linderman, Christopher H. Stock, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Discovering Latent Network Structure in Point Process Data.

Scott W. Linderman, Ryan P. Adams

Proceedings of the 31th International Conference on Machine Learning, 2014