11 citations · 42 across the 14 of their papers we have counts for
15 papers
SortedRL: Accelerating RL Training for LLMs through Online Length-Aware Scheduling
Yiqi Zhang, Huiqiang Jiang, Xufang Luo +7
Scaling reinforcement learning (RL) has shown strong promise for enhancing the reasoning abilities of large language models (LLMs), particularly in tasks requiring long chain-of-th…
What Makes a Good Diffusion Planner for Decision Making?
Haofei Lu, Dongqi Han, Yifei Shen +1
Diffusion models have recently shown significant potential in solving decision-making problems, particularly in generating behavior plans -- also known as diffusion planning. While…
Habitizing Diffusion Planning for Efficient and Effective Decision Making
Haofei Lu, Yifei Shen, Dongsheng Li +2
Diffusion models have shown great promise in decision-making, also known as diffusion planning. However, the slow inference speeds limit their potential for broader real-world appl…
Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and Multi-Task Learning
Zehui Li, Vallijah Subasri, Yifei Shen +4
Large Language Models (LLMs) demonstrate remarkable generalizability across diverse tasks, yet genomic foundation models (GFMs) still require separate finetuning for each downstrea…
Toward Relative Positional Encoding in Spiking Transformers
Changze Lv, Yansen Wang, Dongqi Han +4
Spiking neural networks (SNNs) are bio-inspired networks that mimic how neurons in the brain communicate through discrete spikes, which have great potential in various tasks due to…
Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs
Kai Wang, Siqiang Luo, Caihua Shan +1
Inspired by the success of large language models, there is a trend toward developing graph foundation models to conduct diverse downstream tasks in various domains. However, curren…
Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Path
Xinnan Dai, Qihao Wen, Yifei Shen +4
Large Language Models (LLMs) have achieved great success in various reasoning tasks. In this work, we focus on the graph reasoning ability of LLMs. Although theoretical studies pro…
Compression-Realized Deep Structural Network for Video Quality Enhancement
Hanchi Sun, Xiaohong Liu, Xinyang Jiang +4
This paper focuses on the task of quality enhancement for compressed videos. Although deep network-based video restorers achieve impressive progress, most of the existing methods l…
Understanding and Improving Training-free Loss-based Diffusion Guidance
Yifei Shen, Xinyang Jiang, Yezhen Wang +3
Adding additional control to pretrained diffusion models has become an increasingly popular research area, with extensive applications in computer vision, reinforcement learning, a…
Toward Open-ended Embodied Tasks Solving
William Wei Wang, Dongqi Han, Xufang Luo +4
Empowering embodied agents, such as robots, with Artificial Intelligence (AI) has become increasingly important in recent years. A major challenge is task open-endedness. In practi…
Resurrecting Label Propagation for Graphs with Heterophily and Label Noise
Yao Cheng, Caihua Shan, Yifei Shen +3
Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels…
ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation
Yasheng Sun, Yifan Yang, Houwen Peng +5
While language-guided image manipulation has made remarkable progress, the challenge of how to instruct the manipulation process faithfully reflecting human intentions persists. An…
Dissecting Arbitrary-scale Super-resolution Capability from Pre-trained Diffusion Generative Models
Ruibin Li, Qihua Zhou, Song Guo +5
Diffusion-based Generative Models (DGMs) have achieved unparalleled performance in synthesizing high-quality visual content, opening up the opportunity to improve image super-resol…
Learning Domain Invariant Prompt for Vision-Language Models
Cairong Zhao, Yubin Wang, Xinyang Jiang +4
Prompt learning is one of the most effective and trending ways to adapt powerful vision-language foundation models like CLIP to downstream datasets by tuning learnable prompt vecto…
Recognizing Vector Graphics without Rasterization
Xinyang Jiang, Lu Liu, Caihua Shan +3
In this paper, we consider a different data format for images: vector graphics. In contrast to raster graphics which are widely used in image recognition, vector graphics can be sc…