Statistical Learning
Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization
Langevin dynamics (LD) has been proven to be a powerful technique for optimizing a non-convex objective as an efficient algorithm to find local minima while eventually visiting a global minimum on longer time-scales.
Space-time Mixing Attention for Video Transformer
This paper is on video recognition using Transformers.
Learning outside the Black-Box: The pursuit of interpretable models
Machine Learning has proved its ability to produce accurate models - but the deployment of these models outside the machine learning community has been hindered by the difficulties of interpreting these models.
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies
Deep metric learning plays a key role in various machine learning tasks. Most of the previous works have been confined to sampling from a mini-batch, which cannot precisely characterize the global geometry of the embedding space. Although researchers have developed proxy-and classification-based methods to tackle the sampling issue, those methods inevitably incur a redundant computational cost.