Search Results for author: Leye Wang

Found 15 papers, 4 papers with code

Exploring Context Modeling Techniques on the Spatiotemporal Crowd Flow Prediction

1 code implementation30 Jun 2021 Liyue Chen, Leye Wang

We mainly make three efforts:(i) we develop new taxonomy about both context features and context modeling techniques based on extensive investigations in prevailing STCFP research; (ii) we conduct extensive experiments on seven datasets with hundreds of millions of records to quantitatively evaluate the generalization ability of both distinct context features and context modeling techniques; (iii) we summarize some guidelines for researchers to conveniently utilize context in diverse applications.

Semi-supervised Optimal Transport with Self-paced Ensemble for Cross-hospital Sepsis Early Detection

1 code implementation18 Jun 2021 Ruiqing Ding, Yu Zhou, Jie Xu, Yan Xie, Qiqiang Liang, He Ren, YiXuan Wang, Yanlin Chen, Leye Wang, Man Huang

In SPSSOT, we first extract the same clinical indicators from the source domain (e. g., hospital with rich labeled data) and the target domain (e. g., hospital with little labeled data), then we combine the semi-supervised domain adaptation based on optimal transport theory with self-paced under-sampling to avoid a negative transfer possibly caused by covariate shift and class imbalance.

Domain Adaptation Transfer Learning

FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning

1 code implementation19 Nov 2020 Di Chai, Leye Wang, Kai Chen, Qiang Yang

As an innovative solution for privacy-preserving machine learning (ML), federated learning (FL) is attracting much attention from research and industry areas.

Federated Learning

Federated Crowdsensing: Framework and Challenges

no code implementations6 Nov 2020 Leye Wang, Han Yu, Xiao Han

In particular, we first propose a federated crowdsensing framework, which analyzes the privacy concerns of each crowdsensing stage (i. e., task creation, task assignment, task execution, and data aggregation) and discuss how federated learning techniques may take effect.

Federated Learning

Exploring the Generalizability of Spatio-Temporal Crowd Flow Prediction: Meta-Modeling and an Analytic Framework

no code implementations20 Sep 2020 Leye Wang, Di Chai, Xuanzhe Liu, Liyue Chen, Kai Chen

To fill in this gap, this paper makes two efforts: (i) we propose an analytic framework, called STAnalytic, to qualitatively investigate STCFP approaches regarding their design considerations on various spatial and temporal factors, aiming to make different application-driven approaches comparable; (ii) we construct an extensively large-scale STCFP benchmark datasets with four different scenarios (including ridesharing, bikesharing, metro, and electrical vehicle charging) with up to hundreds of millions of flow records, to quantitatively measure the generalizability of STCFP approaches.

Secure Federated Matrix Factorization

no code implementations12 Jun 2019 Di Chai, Leye Wang, Kai Chen, Qiang Yang

The key principle of federated learning is training a machine learning model without needing to know each user's personal raw private data.

Federated Learning

Multi-Label Robust Factorization Autoencoder and its Application in Predicting Drug-Drug Interactions

no code implementations1 Nov 2018 Xu Chu, Yang Lin, Jingyue Gao, Jiangtao Wang, Yasha Wang, Leye Wang

However, the shallow models leveraging bilinear forms suffer from limitations on capturing complicated nonlinear interactions between drug pairs.

Smart City Development with Urban Transfer Learning

no code implementations5 Aug 2018 Leye Wang, Bin Guo, Qiang Yang

To address this problem, transfer learning can be leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm.

Transfer Learning

Bike Flow Prediction with Multi-Graph Convolutional Networks

no code implementations28 Jul 2018 Di Chai, Leye Wang, Qiang Yang

We propose a new multi-graph convolutional neural network model to predict the bike flow at station-level, where the key novelty is viewing the bike sharing system from the graph perspective.

Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing

no code implementations19 Apr 2018 Leye Wang, wenbin liu, Daqing Zhang, Yasha Wang, En Wang, Yongjian Yang

Since the sensed data from different cells (sub-areas) of the target sensing area will probably lead to diverse levels of inference data quality, cell selection (i. e., choose which cells of the target area to collect sensed data from participants) is a critical issue that will impact the total amount of data that requires to be collected (i. e., data collection costs) for ensuring a certain level of quality.

Transfer Learning

Cross-City Transfer Learning for Deep Spatio-Temporal Prediction

no code implementations1 Feb 2018 Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, Qiang Yang

RegionTrans aims to effectively transfer knowledge from a data-rich source city to a data-scarce target city.

Transfer Learning

Ridesourcing Car Detection by Transfer Learning

no code implementations23 May 2017 Leye Wang, Xu Geng, Jintao Ke, Chen Peng, Xiaojuan Ma, Daqing Zhang, Qiang Yang

Finally, we use the resulting ensemble of RF and CNN to identify the ridesourcing cars in the candidate pool.

Transfer Learning

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