14 citations · 38 across the 12 of their papers we have counts for
6 papers · 1 filter
Results of the NeurIPS 2023 Neural MMO Competition on Multi-task Reinforcement Learning
Joseph Suárez, Kyoung Whan Choe, David Bloomin +22
We present the results of the NeurIPS 2023 Neural MMO Competition, which attracted over 200 participants and submissions. Participants trained goal-conditional policies that genera…
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…
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…
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima
Guangyuan Shi, Jiaxin Chen, Wenlong Zhang +2
This paper considers incremental few-shot learning, which requires a model to continually recognize new categories with only a few examples provided. Our study shows that existing…
Adaptation-Agnostic Meta-Training
Jiaxin Chen, Li-Ming Zhan, Xiao-Ming Wu +1
Many meta-learning algorithms can be formulated into an interleaved process, in the sense that task-specific predictors are learned during inner-task adaptation and meta-parameters…
Variational Metric Scaling for Metric-Based Meta-Learning
Jiaxin Chen, Li-Ming Zhan, Xiao-Ming Wu +1
Metric-based meta-learning has attracted a lot of attention due to its effectiveness and efficiency in few-shot learning. Recent studies show that metric scaling plays a crucial ro…