1 citations · 1 across the 4 of their papers we have counts for
5 papers
Switching Linear Attention
Hyun Dong Lee, Xavier Gonzalez, Nicolas Zucchet +3
Designing expressive sequence layers with efficient inference remains a central challenge in modern machine learning. Standard softmax attention achieves excellent sequence modelin…
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…
Predictability Enables Parallelization of Nonlinear State Space Models
Xavier Gonzalez, Leo Kozachkov, David M. Zoltowski +2
The rise of parallel computing hardware has made it increasingly important to understand which nonlinear state space models can be efficiently parallelized. Recent advances like DE…
Towards Scalable and Stable Parallelization of Nonlinear RNNs
Xavier Gonzalez, Andrew Warrington, Jimmy T. H. Smith +1
Transformers and linear state space models can be evaluated in parallel on modern hardware, but evaluating nonlinear RNNs appears to be an inherently sequential problem. Recently,…