2 citations · 3 across the 4 of their papers we have counts for
4 papers
MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking
Haoning Zhang, Junwei Bao, Haipeng Sun +4
Dialogue state tracking (DST) aims to convert the dialogue history into dialogue states which consist of slot-value pairs. As condensed structural information memorizing all histor…
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…
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…
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…