661 citations · 2.1k across the 57 of their papers we have counts for
11 papers · 1 filter
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
Binghui Wang, Ang Li, Hai Li +1
Graph-based semi-supervised node classification (GraphSSC) has wide applications, ranging from networking and security to data mining and machine learning, etc. However, existing c…
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition
Hsin-Pai Cheng, Feng Liang, Meng Li +5
Scale variance among different sizes of body parts and objects is a challenging problem for visual recognition tasks. Existing works usually design dedicated backbone or apply Neur…
Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs
Houxiang Fan, Binghui Wang, Pan Zhou +6
Link prediction in dynamic graphs (LPDG) is an important research problem that has diverse applications such as online recommendations, studies on disease contagion, organizational…
Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function
Binghui Wang, Tianxiang Zhou, Minhua Lin +5
Graph neural network (GNN), the mainstream method to learn on graph data, is vulnerable to graph evasion attacks, where an attacker slightly perturbing the graph structure can fool…
NASGEM: Neural Architecture Search via Graph Embedding Method
Hsin-Pai Cheng, Tunhou Zhang, Yixing Zhang +7
Neural Architecture Search (NAS) automates and prospers the design of neural networks. Estimator-based NAS has been proposed recently to model the relationship between architecture…
PENNI: Pruned Kernel Sharing for Efficient CNN Inference
Shiyu Li, Edward Hanson, Hai Li +1
Although state-of-the-art (SOTA) CNNs achieve outstanding performance on various tasks, their high computation demand and massive number of parameters make it difficult to deploy t…
Defending against GAN-based Deepfake Attacks via Transformation-aware Adversarial Faces
Chaofei Yang, Lei Ding, Yiran Chen +1
Deepfake represents a category of face-swapping attacks that leverage machine learning models such as autoencoders or generative adversarial networks. Although the concept of the f…
TRP: Trained Rank Pruning for Efficient Deep Neural Networks
Yuhui Xu, Yuxi Li, Shuai Zhang +6
To enable DNNs on edge devices like mobile phones, low-rank approximation has been widely adopted because of its solid theoretical rationale and efficient implementations. Several…
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability
Nathan Inkawhich, Kevin J Liang, Binghui Wang +3
We consider the blackbox transfer-based targeted adversarial attack threat model in the realm of deep neural network (DNN) image classifiers. Rather than focusing on crossing decis…
Transferable Perturbations of Deep Feature Distributions
Nathan Inkawhich, Kevin J Liang, Lawrence Carin +1
Almost all current adversarial attacks of CNN classifiers rely on information derived from the output layer of the network. This work presents a new adversarial attack based on the…
Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification
Huanrui Yang, Minxue Tang, Wei Wen +5
Modern deep neural networks (DNNs) often require high memory consumption and large computational loads. In order to deploy DNN algorithms efficiently on edge or mobile devices, a s…