14 citations · 35 across the 8 of their papers we have counts for
8 papers
UPFL: Unsupervised Personalized Federated Learning towards New Clients
Tiandi Ye, Cen Chen, Yinggui Wang +2
Personalized federated learning has gained significant attention as a promising approach to address the challenge of data heterogeneity. In this paper, we address a relatively unex…
Decentralized Local Updates with Dual-Slow Estimation and Momentum-based Variance-Reduction for Non-Convex Optimization
Kangyang Luo, Kunkun Zhang, Shengbo Zhang +2
Decentralized learning (DL) has recently employed local updates to reduce the communication cost for general non-convex optimization problems. Specifically, local updates require e…
You Can Backdoor Personalized Federated Learning
Tiandi Ye, Cen Chen, Yinggui Wang +2
Existing research primarily focuses on backdoor attacks and defenses within the generic federated learning scenario, where all clients collaborate to train a single global model. A…
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills
Qiushi Sun, Nuo Chen, Jianing Wang +2
Code pre-trained models (CodePTMs) have recently demonstrated a solid capacity to process various software intelligence tasks, e.g., code clone detection, code translation, and cod…
When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario
Chengcheng Han, Liqing Cui, Renyu Zhu +5
Large pre-trained language models (PLMs) have garnered significant attention for their versatility and potential for solving a wide spectrum of natural language processing (NLP) ta…
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition
Chengcheng Han, Renyu Zhu, Jun Kuang +5
Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represent…
Prediction stability as a criterion in active learning
Junyu Liu, Xiang Li, Jin Wang +2
Recent breakthroughs made by deep learning rely heavily on large number of annotated samples. To overcome this shortcoming, active learning is a possible solution. Beside the previ…
Improving One-shot NAS by Suppressing the Posterior Fading
Xiang Li, Chen Lin, Chuming Li +4
There is a growing interest in automated neural architecture search (NAS). To improve the efficiency of NAS, previous approaches adopt weight sharing method to force all models sha…