813 citations · 866 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
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.…
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…
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…
Pseudo Contrastive Learning for Graph-based Semi-supervised Learning
Weigang Lu, Ziyu Guan, Wei Zhao +5
Pseudo Labeling is a technique used to improve the performance of semi-supervised Graph Neural Networks (GNNs) by generating additional pseudo-labels based on confident predictions…
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer
Liyuan Sun, Jianping Gou, Baosheng Yu +2
Knowledge distillation (KD), as an efficient and effective model compression technique, has been receiving considerable attention in deep learning. The key to its success is to tra…