396 citations · 489 across the 14 of their papers we have counts for
5 papers · 1 filter
Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning
Joseph Suárez, Phillip Isola, Kyoung Whan Choe +15
Neural MMO 2.0 is a massively multi-agent environment for reinforcement learning research. The key feature of this new version is a flexible task system that allows users to define…
Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model
Kai Yang, Jian Tao, Jiafei Lyu +6
Using reinforcement learning with human feedback (RLHF) has shown significant promise in fine-tuning diffusion models. Previous methods start by training a reward model that aligns…
Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors
Han Liu, Xingshuo Huang, Xiaotong Zhang +6
Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model predictio…
Recon: Reducing Conflicting Gradients from the Root for Multi-Task Learning
Guangyuan Shi, Qimai Li, Wenlong Zhang +2
A fundamental challenge for multi-task learning is that different tasks may conflict with each other when they are solved jointly, and a cause of this phenomenon is conflicting gra…
Simple yet Effective Gradient-Free Graph Convolutional Networks
Yulin Zhu, Xing Ai, Qimai Li +2
Linearized Graph Neural Networks (GNNs) have attracted great attention in recent years for graph representation learning. Compared with nonlinear Graph Neural Network (GNN) models,…