15 citations · 39 across the 7 of their papers we have counts for
7 papers
Personalized Federated Learning with Hidden Information on Personalized Prior
Mingjia Shi, Yuhao Zhou, Qing Ye +1
Federated learning (FL for simplification) is a distributed machine learning technique that utilizes global servers and collaborative clients to achieve privacy-preserving global m…
Partial Differential Equations Meet Deep Neural Networks: A Survey
Shudong Huang, Wentao Feng, Chenwei Tang +1
Many problems in science and engineering can be represented by a set of partial differential equations (PDEs) through mathematical modeling. Mechanism-based computation following P…
Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition
Youcheng Huang, Wenqiang Lei, Jie Fu +1
Incorporating large-scale pre-trained models with the prototypical neural networks is a de-facto paradigm in few-shot named entity recognition. Existing methods, unfortunately, are…
Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy
Wenqiang Lei, Yao Zhang, Feifan Song +5
Proactive dialogue system is able to lead the conversation to a goal topic and has advantaged potential in bargain, persuasion and negotiation. Current corpus-based learning manner…
DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg
Yuhao Zhou, Minjia Shi, Yuxin Tian +2
Federated learning (FL) is identified as a crucial enabler for large-scale distributed machine learning (ML) without the need for local raw dataset sharing, substantially reducing…
Cluster-based Contrastive Disentangling for Generalized Zero-Shot Learning
Yi Gao, Chenwei Tang, Jiancheng Lv
Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen classes by training only the seen classes, in which the instances of unseen classes tend to be biased t…