no code implementations • 25 May 2023 • Yixiu Zhao, Scott W. Linderman
Here, we revisit SVAEs using modern machine learning tools and demonstrate their advantages over more general alternatives in terms of both accuracy and efficiency.
2 code implementations • 9 Aug 2022 • Jimmy T. H. Smith, Andrew Warrington, Scott W. Linderman
Models using structured state space sequence (S4) layers have achieved state-of-the-art performance on long-range sequence modeling tasks.
Ranked #3 on Long-range modeling on LRA
1 code implementation • 13 Jan 2022 • Yixin Wang, Anthony Degleris, Alex H. Williams, Scott W. Linderman
This construction is similar to Bayesian nonparametric mixture models like the Dirichlet process mixture model (DPMM) in that the number of latent events (i. e. clusters) is a random variable, but the point process formulation makes the NSP especially well suited to modeling spatiotemporal data.
1 code implementation • NeurIPS 2021 • Jimmy T. H. Smith, Scott W. Linderman, David Sussillo
The results are a trained SLDS variant that closely approximates the RNN, an auxiliary function that can produce a fixed point for each point in state-space, and a trained nonlinear RNN whose dynamics have been regularized such that its first-order terms perform the computation, if possible.
2 code implementations • NeurIPS 2021 • Alex H. Williams, Erin Kunz, Simon Kornblith, Scott W. Linderman
In doing so, we identify relationships between neural representations that are interpretable in terms of anatomical features and model performance.
no code implementations • 8 Mar 2021 • Alex H. Williams, Scott W. Linderman
Individual neurons often produce highly variable responses over nominally identical trials, reflecting a mixture of intrinsic "noise" and systematic changes in the animal's cognitive and behavioral state.
1 code implementation • 20 Jan 2021 • Xinwei Yu, Matthew S. Creamer, Francesco Randi, Anuj K. Sharma, Scott W. Linderman, Andrew M. Leifer
The same pre-trained model both tracks neurons across time and identifies corresponding neurons across individuals.
1 code implementation • NeurIPS 2020 • Alex H. Williams, Anthony Degleris, Yixin Wang, Scott W. Linderman
Sparse sequences of neural spikes are posited to underlie aspects of working memory, motor production, and learning.
1 code implementation • 13 Jan 2020 • David M. Zoltowski, Jonathan W. Pillow, Scott W. Linderman
An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision.
1 code implementation • NeurIPS 2019 • Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna Wallach
This paper presents the Poisson-randomized gamma dynamical system (PRGDS), a model for sequentially observed count tensors that encodes a strong inductive bias toward sparsity and burstiness.
1 code implementation • 13 Dec 2018 • Wesley Tansey, Kathy Li, Haoran Zhang, Scott W. Linderman, Raul Rabadan, David M. Blei, Chris H. Wiggins
Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology.
Applications
1 code implementation • ICLR 2019 • Josue Nassar, Scott W. Linderman, Monica Bugallo, Il Memming Park
Many real-world systems studied are governed by complex, nonlinear dynamics.
no code implementations • 26 Oct 2017 • Scott W. Linderman, Gonzalo E. Mena, Hal Cooper, Liam Paninski, John P. Cunningham
Many matching, tracking, sorting, and ranking problems require probabilistic reasoning about possible permutations, a set that grows factorially with dimension.
1 code implementation • 31 May 2017 • Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei
The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior.
1 code implementation • 26 Oct 2016 • Scott W. Linderman, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski, Matthew J. Johnson
Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics.
2 code implementations • NeurIPS 2016 • Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow
Neural circuits contain heterogeneous groups of neurons that differ in type, location, connectivity, and basic response properties.
2 code implementations • 18 Oct 2016 • Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, David M. Blei
Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization to sidestep intractable expectations.
1 code implementation • 12 Jul 2015 • Scott W. Linderman, Ryan P. Adams
We build on previous work that has taken a Bayesian approach to this problem, specifying prior distributions over the latent network structure and a likelihood of observed activity given this network.
1 code implementation • 18 Jun 2015 • Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams
Many practical modeling problems involve discrete data that are best represented as draws from multinomial or categorical distributions.
no code implementations • 27 Nov 2014 • Scott W. Linderman, Matthew J. Johnson, Matthew A. Wilson, Zhe Chen
Rodent hippocampal population codes represent important spatial information about the environment during navigation.
no code implementations • NeurIPS 2014 • Scott W. Linderman, Christopher H. Stock, Ryan P. Adams
Learning and memory in the brain are implemented by complex, time-varying changes in neural circuitry.
no code implementations • 4 Feb 2014 • Scott W. Linderman, Ryan P. Adams
Networks play a central role in modern data analysis, enabling us to reason about systems by studying the relationships between their parts.