661 citations · 2.1k across the 57 of their papers we have counts for
7 papers · 1 filter
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…