Deep Learning
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms Anonymous Author(s) Affiliation Address email
While there has been much attention on the theoretical aspect of the traditional i.i.d.
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms Lam M. Nguyen
Stochastic gradient descent (SGD) algorithm is the method of choice in many machine learning tasks thanks to its scalability and efficiency in dealing with large-scale problems. In this paper, we focus on the shuffling version of SGD which matches the mainstream practical heuristics. We show the convergence to a global solution of shuffling SGD for a class of non-convex functions under over-parameterized settings.
AI Agents Are Taking America by Storm
The post-chatbot era has begun. Americans are living in parallel AI universes. For much of the country, AI has come to mean ChatGPT, Google's AI overviews, and the slop that now clogs social-media feeds. Meanwhile, tech hobbyists are becoming radicalized by bots that can work for hours on end, collapsing months of work into weeks, or weeks into an afternoon. Recently, more people have started to play around with tools such as Claude Code .
Lisa: Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning Attack
Fine-tuning services for Large Language Models (LLMs) have emerged as a new paradigm.
