79 citations · 156 across the 15 of their papers we have counts for
6 papers · 1 filter
Defending Against Poisoning Attacks in Federated Learning with Blockchain
Nanqing Dong, Zhipeng Wang, Jiahao Sun +3
In the era of deep learning, federated learning (FL) presents a promising approach that allows multi-institutional data owners, or clients, to collaboratively train machine learnin…
Revisiting Vicinal Risk Minimization for Partially Supervised Multi-Label Classification Under Data Scarcity
Nanqing Dong, Jiayi Wang, Irina Voiculescu
Due to the high human cost of annotation, it is non-trivial to curate a large-scale medical dataset that is fully labeled for all classes of interest. Instead, it would be convenie…
Federated Contrastive Learning for Decentralized Unlabeled Medical Images
Nanqing Dong, Irina Voiculescu
A label-efficient paradigm in computer vision is based on self-supervised contrastive pre-training on unlabeled data followed by fine-tuning with a small number of labels. Making p…
Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification
Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu +1
Entanglement is a physical phenomenon, which has fueled recent successes of quantum algorithms. Although quantum neural networks (QNNs) have shown promising results in solving simp…
Adversarial Domain Adaptation Being Aware of Class Relationships
Zeya Wang, Baoyu Jing, Yang Ni +3
Adversarial training is a useful approach to promote the learning of transferable representations across the source and target domains, which has been widely applied for domain ada…
Toward Understanding the Impact of Staleness in Distributed Machine Learning
Wei Dai, Yi Zhou, Nanqing Dong +2
Many distributed machine learning (ML) systems adopt the non-synchronous execution in order to alleviate the network communication bottleneck, resulting in stale parameters that do…