661 citations · 2.1k across the 57 of their papers we have counts for
57 papers
OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection
Jingyang Zhang, Jingkang Yang, Pengyun Wang +9
Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world intelligent systems. Despite the emergence of an increasing number of OOD detection methods…
SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection
Jingyang Zhang, Nathan Inkawhich, Randolph Linderman +3
Building up reliable Out-of-Distribution (OOD) detectors is challenging, often requiring the use of OOD data during training. In this work, we develop a data-driven approach which…
PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud
Tunhou Zhang, Mingyuan Ma, Feng Yan +2
The interaction and dimension of points are two important axes in designing point operators to serve hierarchical 3D models. Yet, these two axes are heterogeneous and challenging t…
Federated Unsupervised Visual Representation Learning via Exploiting General Content and Personal Style
Yuewei Yang, Jingwei Sun, Ang Li +2
Discriminative unsupervised learning methods such as contrastive learning have demonstrated the ability to learn generalized visual representations on centralized data. It is nonet…
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification
Randolph Linderman, Jingyang Zhang, Nathan Inkawhich +2
Machine learning methods must be trusted to make appropriate decisions in real-world environments, even when faced with out-of-distribution (OOD) samples. Many current approaches s…
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
Jianyi Zhang, Ang Li, Minxue Tang +6
Due to limited communication capacities of edge devices, most existing federated learning (FL) methods randomly select only a subset of devices to participate in training for each…
Tunable Hybrid Proposal Networks for the Open World
Matthew Inkawhich, Nathan Inkawhich, Hai Li +1
Current state-of-the-art object proposal networks are trained with a closed-world assumption, meaning they learn to only detect objects of the training classes. These models fail t…
NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
Tunhou Zhang, Dehua Cheng, Yuchen He +7
The rise of deep neural networks offers new opportunities in optimizing recommender systems. However, optimizing recommender systems using deep neural networks requires delicate ar…
Privacy Leakage of Adversarial Training Models in Federated Learning Systems
Jingyang Zhang, Yiran Chen, Hai Li
Adversarial Training (AT) is crucial for obtaining deep neural networks that are robust to adversarial attacks, yet recent works found that it could also make models more vulnerabl…
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective
Jingwei Sun, Ang Li, Louis DiValentin +3
Federated learning (FL) is a popular distributed learning framework that trains a global model through iterative communications between a central server and edge devices. Recent wo…
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments
Jingyang Zhang, Nathan Inkawhich, Randolph Linderman +2
Many real-world scenarios in which DNN-based recognition systems are deployed have inherently fine-grained attributes (e.g., bird-species recognition, medical image classification)…
Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?
Nathan Inkawhich, Kevin J Liang, Jingyang Zhang +3
We design blackbox transfer-based targeted adversarial attacks for an environment where the attacker's source model and the target blackbox model may have disjoint label spaces and…
The Untapped Potential of Off-the-Shelf Convolutional Neural Networks
Matthew Inkawhich, Nathan Inkawhich, Eric Davis +2
Over recent years, a myriad of novel convolutional network architectures have been developed to advance state-of-the-art performance on challenging recognition tasks. As computatio…
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning
Minxue Tang, Xuefei Ning, Yitu Wang +4
Client-wise data heterogeneity is one of the major issues that hinder effective training in federated learning (FL). Since the data distribution on each client may vary dramaticall…
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization
Huanrui Yang, Lin Duan, Yiran Chen +1
Mixed-precision quantization can potentially achieve the optimal tradeoff between performance and compression rate of deep neural networks, and thus, have been widely investigated.…
On Provable Backdoor Defense in Collaborative Learning
Ximing Qiao, Yuhua Bai, Siping Hu +3
As collaborative learning allows joint training of a model using multiple sources of data, the security problem has been a central concern. Malicious users can upload poisoned data…
Hermes: Decentralized Dynamic Spectrum Access System for Massive Devices Deployment in 5G
Zhihui Gao, Ang Li, Yunfan Gao +2
With the incoming 5G network, the ubiquitous Internet of Things (IoT) devices can benefit our daily life, such as smart cameras, drones, etc. With the introduction of the millimete…
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…