most citedCSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking

1 citations · 1 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CL2022★ 1 cited

CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking

Haoning Zhang, Junwei Bao, Haipeng Sun +3

Few-shot dialogue state tracking (DST) is a realistic problem that trains the DST model with limited labeled data. Existing few-shot methods mainly transfer knowledge learned from…

cs.LG2022

Personalizing or Not: Dynamically Personalized Federated Learning with Incentives

Zichen Ma, Yu Lu, Wenye Li +1

Personalized federated learning (FL) facilitates collaborations between multiple clients to learn personalized models without sharing private data. The mechanism mitigates the stat…

cs.LG2021

Towards Heterogeneous Clients with Elastic Federated Learning

Zichen Ma, Yu Lu, Zihan Lu +3

Federated learning involves training machine learning models over devices or data silos, such as edge processors or data warehouses, while keeping the data local. Training in heter…