5 citations · 10 across the 9 of their papers we have counts for
7 papers · 1 filter
HyperNAS: Enhancing Architecture Representation for NAS Predictor via Hypernetwork
Jindi Lv, Yuhao Zhou, Yuxin Tian +3
Time-intensive performance evaluations significantly impede progress in Neural Architecture Search (NAS). To address this, neural predictors leverage surrogate models trained on pr…
Deploying Models to Non-participating Clients in Federated Learning without Fine-tuning: A Hypernetwork-based Approach
Yuhao Zhou, Jindi Lv, Yuxin Tian +3
Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative learning, yet data heterogeneity remains a critical challenge. While existing metho…
Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints
Yuhao Zhou, Yuxin Tian, Jindi Lv +5
In the realm of high-frequency data streams, achieving real-time learning within varying memory constraints is paramount. This paper presents Ferret, a comprehensive framework desi…
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
Yuxin Tian, Mouxing Yang, Yuhao Zhou +5
Conventional federated learning (FL) heavily depends on high-quality labels, which are often impractical in the real world, leading to the federated label-noise (F-LN) problem. Wor…
MSD: A RG Flow-Based Regularization for GAN Training with Limited Data
Jian Wang, Xin Lan, Yuxin Tian +1
Generative adversarial networks (GANs) have made impressive advances in image generation, but they often require large-scale training data to avoid degradation caused by discrimina…
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning
Mingjia Shi, Yuhao Zhou, Kai Wang +4
Classical federated learning (FL) enables training machine learning models without sharing data for privacy preservation, but heterogeneous data characteristic degrades the perform…
Communication-Efficient Federated Learning with Compensated Overlap-FedAvg
Yuhao Zhou, Ye Qing, Jiancheng Lv
Petabytes of data are generated each day by emerging Internet of Things (IoT), but only few of them can be finally collected and used for Machine Learning (ML) purposes due to the…