5 papers
Mechanistically Interpreting the Role of Sample Difficulty in RLVR for LLMs
Yue Cheng, Jiajun Zhang, Xiaohui Gao +3
Reinforcement Learning with Verifiable Reward (RLVR) is empirically shown to notably enhance the reasoning performance of large language models (LLMs), particularly in mathematics…
VSE-MOT: Multi-Object Tracking in Low-Quality Video Scenes Guided by Visual Semantic Enhancement
Jun Du, Weiwei Xing, Ming Li +1
Current multi-object tracking (MOT) algorithms typically overlook issues inherent in low-quality videos, leading to significant degradation in tracking performance when confronted…
LCGC: Learning from Consistency Gradient Conflicting for Class-Imbalanced Semi-Supervised Debiasing
Weiwei Xing, Yue Cheng, Hongzhu Yi +5
Classifiers often learn to be biased corresponding to the class-imbalanced dataset, especially under the semi-supervised learning (SSL) set. While previous work tries to appropriat…
LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data
Jiajun Zhang, Boyang Qiang, Xiaoyu Guo +3
Discovering the underlying Directed Acyclic Graph (DAG) from time series observational data is highly challenging due to the dynamic nature and complex nonlinear interactions betwe…
LinBridge: A Learnable Framework for Interpreting Nonlinear Neural Encoding Models
Xiaohui Gao, Yue Cheng, Peiyang Li +7
Neural encoding of artificial neural networks (ANNs) links their computational representations to brain responses, offering insights into how the brain processes information. Curre…