Statistical Learning
Variational Inference with Gaussian Score Matching
V ariational inference (VI) is a method to approximate the computationally intractable posterior distributions that arise in Bayesian statistics.
Gradient Information for Representation and Modeling
Jie Ding, Robert Calderbank, Vahid Tarokh
Neural Information Processing Systems http://nips.cc/
Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs
Denis Mazur, Vage Egiazarian, Stanislav Morozov, Artem Babenko
In this paper, we aim to eliminate the inductive bias imposed by the embedding space geometry.
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
Youjie Li, Mingchao Yu, Songze Li, Salman Avestimehr, Nam Sung Kim, Alexander Schwing
In this paper, we carefully analyze the AllReduce based setup, propose timing models which include network latency, bandwidth, cluster size and compute time, and demonstrate that a pipelined training with a width oftwocombines thebest ofboth synchronous and asynchronous training.
