6 citations · 10 across the 5 of their papers we have counts for
5 papers
Incorporating uncertainty quantification into travel mode choice modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework
Shuwen Zheng, Zhou Fang, Liang Zhao
Existing deep learning approaches for travel mode choice modeling fail to inform modelers about their prediction uncertainty. Even when facing scenarios that are out of the distrib…
Effective filtering approach for joint parameter-state estimation in SDEs via Rao-Blackwellization and modularization
Zhou Fang, Ankit Gupta, Mustafa Khammash
Stochastic filtering is a vibrant area of research in both control theory and statistics, with broad applications in many scientific fields. Despite its extensive historical develo…
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing
Xinyu Zuo, Haijin Liang, Ning Jing +3
Fine-grained entity typing (FET) aims to deduce specific semantic types of the entity mentions in text. Modern methods for FET mainly focus on learning what a certain type looks li…
Fast Heterogeneous Federated Learning with Hybrid Client Selection
Guangyuan Shen, Dehong Gao, Duanxiao Song +5
Client selection schemes are widely adopted to handle the communication-efficient problems in recent studies of Federated Learning (FL). However, the large variance of the model up…
Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection
Guangyuan Shen, Dehong Gao, Libin Yang +4
Client selection strategies are widely adopted to handle the communication-efficient problem in recent studies of Federated Learning (FL). However, due to the large variance of the…