activity
20142025
most citedClassification with Noisy Labels by Importance Reweighting

813 citations · 866 across the 15 of their papers we have counts for

collaborators

15 papers

cs.CV2025

From Hindsight to Foresight: Self-Encouraged Hindsight Distillation for Knowledge-based Visual Question Answering

Yu Zhao, Ying Zhang, Xuhui Sui +4

Knowledge-based Visual Question Answering (KBVQA) necessitates external knowledge incorporation beyond cross-modal understanding. Existing KBVQA methods either utilize implicit kno…

cs.LG2025

CertDW: Towards Certified Dataset Ownership Verification via Conformal Calibration

Ting Qiao, Yiming Li, Jianbin Li +5

Deep neural networks (DNNs) rely heavily on high-quality open-source datasets (e.g., ImageNet) for their success, making dataset ownership verification (DOV) crucial for protecting…

cs.LG2025

Adaptively Point-weighting Curriculum Learning

Wensheng Li, Yichao Tian, Hao Wang +4

Curriculum learning (CL) mimics human learning, in which easy samples are learned first, followed by harder samples, and has become an effective method for training deep networks.…

cs.CR2025

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment

Kun Wang, Guibin Zhang, Zhenhong Zhou +100

The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communi…

cs.LG2024★ 2 cited

LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit

Ruihao Gong, Yang Yong, Shiqiao Gu +5

Recent advancements in large language models (LLMs) are propelling us toward artificial general intelligence with their remarkable emergent abilities and reasoning capabilities. Ho…

cs.LG2023★ 1 cited

Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion

Anke Tang, Xianglin Luo, Li Shen +5

Merging models fine-tuned from a common, extensively pre-trained large model but specialized for different tasks has been demonstrated as a cheap and scalable strategy to construct…