1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.LG2020★ 1 cited
Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling
Giulio Franzese, Rosa Candela, Dimitrios Milios +2
In this work we define a unified mathematical framework to deepen our understanding of the role of stochastic gradient (SG) noise on the behavior of Markov chain Monte Carlo sampli…
cs.LG2019
Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD
Rosa Candela, Giulio Franzese, Maurizio Filippone +1
Large scale machine learning is increasingly relying on distributed optimization, whereby several machines contribute to the training process of a statistical model. In this work w…