Overview
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Shupeng Gui, Haotao N. Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu
Previous literature suggested that the goals of robustness and compactness might sometimes contradict.
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
A common feature of these applications is that there are multiple decision makers interacting with each other in a common environment.
Contrastive Sampling Chains in Diffusion Models
The code is available at Contrastive-Sampling .
References
Sparse mixture-of-experts are domain generalizable learners.
Large Language Models are Visual Reasoning Coordinators Liangyu Chen,, Bo Li
Existing methods like ensemble still struggle to aggregate these models with the desired higher-order communications. In this work, we propose Cola, a novel paradigm that coordinates multiple VLMs for visual reasoning.
Iterative Reachability Estimation for Safe Reinforcement Learning
We theoretically establish that our algorithms almost surely converge to locally optimal policies of our safe optimization framework.