151 citations · 270 across the 6 of their papers we have counts for
6 papers
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees
Ruqi Zhang, Christopher De Sa
Gibbs sampling is a Markov chain Monte Carlo method that is often used for learning and inference on graphical models. Minibatching, in which a small random subset of the graph is…
QPyTorch: A Low-Precision Arithmetic Simulation Framework
Tianyi Zhang, Zhiqiu Lin, Guandao Yang +1
Low-precision training reduces computational cost and produces efficient models. Recent research in developing new low-precision training algorithms often relies on simulation to e…
PipeMare: Asynchronous Pipeline Parallel DNN Training
Bowen Yang, Jian Zhang, Jonathan Li +3
Pipeline parallelism (PP) when training neural networks enables larger models to be partitioned spatially, leading to both lower network communication and overall higher hardware u…
Distributed Learning with Sublinear Communication
Jayadev Acharya, Christopher De Sa, Dylan J. Foster +1
In distributed statistical learning, samples are split across machines and a learner wishes to use minimal communication to learn as well as if the examples were on a singl…
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Ritchie Zhao, Yuwei Hu, Jordan Dotzel +2
Quantization can improve the execution latency and energy efficiency of neural networks on both commodity GPUs and specialized accelerators. The majority of existing literature foc…
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems
Christopher De Sa, Kunle Olukotun, Christopher Ré
Stochastic gradient descent (SGD) on a low-rank factorization is commonly employed to speed up matrix problems including matrix completion, subspace tracking, and SDP relaxation. I…