3k citations · 24.2k across the 299 of their papers we have counts for
5 papers · 2 filters
SparkNet: Training Deep Networks in Spark
Philipp Moritz, Robert Nishihara, Ion Stoica +1
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a clus…
Perturbed Iterate Analysis for Asynchronous Stochastic Optimization
Horia Mania, Xinghao Pan, Dimitris Papailiopoulos +3
We introduce and analyze stochastic optimization methods where the input to each gradient update is perturbed by bounded noise. We show that this framework forms the basis of a uni…
On the accuracy of self-normalized log-linear models
Jacob Andreas, Maxim Rabinovich, Dan Klein +1
Calculation of the log-normalizer is a major computational obstacle in applications of log-linear models with large output spaces. The problem of fast normalizer computation has th…
Variational consensus Monte Carlo
Maxim Rabinovich, Elaine Angelino, Michael I. Jordan
Practitioners of Bayesian statistics have long depended on Markov chain Monte Carlo (MCMC) to obtain samples from intractable posterior distributions. Unfortunately, MCMC algorithm…
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
Ryan Giordano, Tamara Broderick, Michael Jordan
Mean field variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of…