15 citations · 15 across the 11 of their papers we have counts for
11 papers
PLAT: Sparse Timed Keyframe Motion Tracking for Humanoid Control via Privileged Latent Transition Learning
Zepeng Wang, Jiangxing Wang, Chao Ma +2
Humanoid motion tracking policies rely on dense frame-by-frame references, limiting their use as high-level motion controllers for planning and interactive motion generation. We st…
BiRD: A Bidirectional Ranking Defense Mechanism for Retrieval Augmented Generation
Chengcai Gao, Zhihong Sun, Xiaochuan Shi +2
The growing adoption of Retrieval-Augmented Generation (RAG) has led to a rise in adversarial attacks. Existing defenses, relying on semantic analysis or voting, face a trade-off b…
Single-Sample Black-Box Membership Inference Attack against Vision-Language Models via Cross-modal Semantic Alignment
Jiaqing Li, Yajuan Lu, Xiaochuan Shi +3
Vision-Language Models (VLMs) have achieved remarkable success, yet their reliance on massive datasets and unintended memorization of training data raise significant data security…
General Humanoid Whole-Body Control via Pretraining and Fast Adaptation
Zepeng Wang, Jiangxing Wang, Shiqing Yao +8
Learning a general whole-body controller for humanoid robots remains challenging due to the diversity of motion distributions, the difficulty of fast adaptation, and the need for r…
Efficient Multi-turn RL for GUI Agents via Decoupled Training and Adaptive Data Curation
Pengxiang Li, Zechen Hu, Zirui Shang +15
Vision-language model (VLM) based GUI agents show promise for automating complex desktop and mobile tasks, but face significant challenges in applying reinforcement learning (RL):…
MDSAM:Memory-Driven Sparse Attention Matrix for LVLMs Hallucination Mitigation
Shuaiye Lu, Linjiang Zhou, Xiaochuan Shi
Hallucinations in large vision-language models (LVLMs) often stem from the model's sensitivity to image tokens during decoding, as evidenced by attention peaks observed when genera…
Multi-round jailbreak attack on large language models
Yihua Zhou, Xiaochuan Shi
Ensuring the safety and alignment of large language models (LLMs) with human values is crucial for generating responses that are beneficial to humanity. While LLMs have the capabil…
AdaptGrad: Adaptive Sampling to Reduce Noise
Linjiang Zhou, Chao Ma, Zepeng Wang +2
Gradient Smoothing is an efficient approach to reducing noise in gradient-based model explanation method. SmoothGrad adds Gaussian noise to mitigate much of these noise. However, t…
: Towards Effective and Efficient Cost Function Design for Safe Reinforcement Learning via Large Language Model
Zepeng Wang, Chao Ma, Linjiang Zhou +4
Different classes of safe reinforcement learning algorithms have shown satisfactory performance in various types of safety requirement scenarios. However, the existing methods main…
Axiomatization of Gradient Smoothing in Neural Networks
Linjiang Zhou, Xiaochuan Shi, Chao Ma +1
Gradients play a pivotal role in neural networks explanation. The inherent high dimensionality and structural complexity of neural networks result in the original gradients contain…
Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction
Shunjie Chen, Xiaochuan Shi, Jingye Li +4
Emotion cause pair extraction (ECPE), as one of the derived subtasks of emotion cause analysis (ECA), shares rich inter-related features with emotion extraction (EE) and cause extr…