The mathematics under the hood: optimization methods, convergence guarantees, and the theory that explains when and why deep learning works. Explainers that take proofs seriously without losing the reader.
Pierre C. Bellec (Rutgers) and Takuya Koriyama (Chicago Booth) prove that a simple, fully observable formula consistently estimates the prediction error of any Huber-class M-estimator in high dimensions — and that minimizing it over a grid of scale parameters automatically…
Researchers from Michelin and the Centre Borelli at ENS Paris-Saclay have built a smarter way to make neural networks say “I don’t know” — by maximizing the diversity of possible network weights rather than relying on standard regularization that quietly…
Why Your AI Says It’s Confident When It Shouldn’t Be — And How MaxWEnt Fixes It Read More »
Most modern semi supervised learning systems, FixMatch and FreeMatch among the best known, work the same basic way. Take an unlabeled image, run it through the model with a light augmentation, and if the model’s confidence in its own prediction…
Long Tailed Weights Beat Thresholds For Pseudo Labels Read More »