Statistical Learning
Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds (Supplementary material)
A.1 Algorithms used in the literature A.1.1 The T
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization
Large scale distributed optimization has become the default tool for the training of supervised machine learning models with a large number of parameters and training data.
Imbalance T rouble: Revisiting Neural-Collapse Geometry Christos Thrampoulidis, Ganesh R. Kini
Neural Collapse refers to the remarkable structural properties characterizing the geometry of class embeddings and classifier weights, found by deep nets when trained beyond zero training error. However, this characterization only holds for balanced data.
Maximum a posteriori natural scene reconstruction from retinal ganglion cells with deep denoiser priors
Previous work has probed the content of these signals by directly reconstructing images from retinal activity using linear regression or nonlinear regression with neural networks.
