68 citations · 142 across the 9 of their papers we have counts for
9 papers
Dataset Ownership Verification in Contrastive Pre-trained Models
Yuechen Xie, Jie Song, Mengqi Xue +5
High-quality open-source datasets, which necessitate substantial efforts for curation, has become the primary catalyst for the swift progress of deep learning. Concurrently, protec…
LG-CAV: Train Any Concept Activation Vector with Language Guidance
Qihan Huang, Jie Song, Mengqi Xue +6
Concept activation vector (CAV) has attracted broad research interest in explainable AI, by elegantly attributing model predictions to specific concepts. However, the training of C…
Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation
Tongya Zheng, Xinchao Wang, Zunlei Feng +6
Temporal graphs exhibit dynamic interactions between nodes over continuous time, whose topologies evolve with time elapsing. The whole temporal neighborhood of nodes reveals the va…
Transition Propagation Graph Neural Networks for Temporal Networks
Tongya Zheng, Zunlei Feng, Tianli Zhang +6
Researchers of temporal networks (e.g., social networks and transaction networks) have been interested in mining dynamic patterns of nodes from their diverse interactions. Inspired…
Learning Dynamic Preference Structure Embedding From Temporal Networks
Tongya Zheng, Zunlei Feng, Yu Wang +6
The dynamics of temporal networks lie in the continuous interactions between nodes, which exhibit the dynamic node preferences with time elapsing. The challenges of mining temporal…
Real-time intelligent big data processing: technology, platform, and applications
Tongya Zheng, Gang Chen, Xinyu Wang +3
Human beings keep exploring the physical space using information means. Only recently, with the rapid development of information technologies and the increasing accumulation of dat…
Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling
Peixin Zhang, Jingyi Wang, Jun Sun +5
Although deep learning has demonstrated astonishing performance in many applications, there are still concerns about its dependability. One desirable property of deep learning appl…
Contrastive Model Inversion for Data-Free Knowledge Distillation
Gongfan Fang, Jie Song, Xinchao Wang +3
Model inversion, whose goal is to recover training data from a pre-trained model, has been recently proved feasible. However, existing inversion methods usually suffer from the mod…
KDExplainer: A Task-oriented Attention Model for Explaining Knowledge Distillation
Mengqi Xue, Jie Song, Xinchao Wang +3
Knowledge distillation (KD) has recently emerged as an efficacious scheme for learning compact deep neural networks (DNNs). Despite the promising results achieved, the rationale th…