19 citations · 35 across the 12 of their papers we have counts for
13 papers
One-Step Flow Policy: Self-Distillation for Fast Visuomotor Policies
Shaolong Li, Lichao Sun, Yongchao Chen
Generative flow and diffusion models provide the continuous, multimodal action distributions needed for high-precision robotic policies. However, their reliance on iterative sampli…
Decision Flow Policy Optimization
Jifeng Hu, Sili Huang, Siyuan Guo +6
In recent years, generative models have shown remarkable capabilities across diverse fields, including images, videos, language, and decision-making. By applying powerful generativ…
Analytic Energy-Guided Policy Optimization for Offline Reinforcement Learning
Jifeng Hu, Sili Huang, Zhejian Yang +6
Conditional decision generation with diffusion models has shown powerful competitiveness in reinforcement learning (RL). Recent studies reveal the relation between energy-function-…
Solving Continual Offline RL through Selective Weights Activation on Aligned Spaces
Jifeng Hu, Sili Huang, Li Shen +7
Continual offline reinforcement learning (CORL) has shown impressive ability in diffusion-based lifelong learning systems by modeling the joint distributions of trajectories. Howev…
Continual Diffuser (CoD): Mastering Continual Offline Reinforcement Learning with Experience Rehearsal
Jifeng Hu, Li Shen, Sili Huang +5
Artificial neural networks, especially recent diffusion-based models, have shown remarkable superiority in gaming, control, and QA systems, where the training tasks' datasets are u…
Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling
Sili Huang, Jifeng Hu, Zhejian Yang +5
Recent works have shown the remarkable superiority of transformer models in reinforcement learning (RL), where the decision-making problem is formulated as sequential generation. T…
In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought
Sili Huang, Jifeng Hu, Hechang Chen +2
In-context learning is a promising approach for offline reinforcement learning (RL) to handle online tasks, which can be achieved by providing task prompts. Recent works demonstrat…
Learning Generalizable Agents via Saliency-Guided Features Decorrelation
Sili Huang, Yanchao Sun, Jifeng Hu +5
In visual-based Reinforcement Learning (RL), agents often struggle to generalize well to environmental variations in the state space that were not observed during training. The var…
Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial Examples
Qiufan Ji, Lin Wang, Cong Shi +3
Deep Neural Networks (DNNs) for 3D point cloud recognition are vulnerable to adversarial examples, threatening their practical deployment. Despite the many research endeavors have…
MM-LIMA: Less Is More for Alignment in Multi-Modal Datasets
Lai Wei, Xiaozhe Li, Zihao Jiang +2
Multimodal large language models are typically trained in two stages: first pre-training on image-text pairs, and then fine-tuning using supervised vision-language instruction data…
Evaluating Large Language Models for Radiology Natural Language Processing
Zhengliang Liu, Tianyang Zhong, Yiwei Li +43
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they hav…
Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning
Jifeng Hu, Yanchao Sun, Sili Huang +6
Recent works have shown the potential of diffusion models in computer vision and natural language processing. Apart from the classical supervised learning fields, diffusion models…
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning
Siyuan Guo, Yanchao Sun, Jifeng Hu +5
Offline reinforcement learning (RL) provides a promising solution to learning an agent fully relying on a data-driven paradigm. However, constrained by the limited quality of the o…