44 citations · 44 across the 4 of their papers we have counts for
5 papers
A Unifying Framework for Parallelizing Sequential Models with Linear Dynamical Systems
Xavier Gonzalez, E. Kelly Buchanan, Hyun Dong Lee +6
Harnessing parallelism in seemingly sequential models is a central challenge for modern machine learning. Several approaches have been proposed for evaluating sequential processes…
Parallelizing MCMC Across the Sequence Length
David M. Zoltowski, Skyler Wu, Xavier Gonzalez +2
Markov chain Monte Carlo (MCMC) methods are foundational algorithms for Bayesian inference and probabilistic modeling. However, most MCMC algorithms are inherently sequential and t…
Brain-to-Text Benchmark '24: Lessons Learned
Francis R. Willett, Jingyuan Li, Trung Le +13
Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the abili…
Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems
Amber Hu, David Zoltowski, Aditya Nair +3
Understanding how the collective activity of neural populations relates to computation and ultimately behavior is a key goal in neuroscience. To this end, statistical methods which…
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
Felix Pei, Joel Ye, David Zoltowski +13
Advances in neural recording present increasing opportunities to study neural activity in unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this…