Search Results for author: Leye Wang

Found 27 papers, 9 papers with code

Understanding Self-Supervised Pretraining with Part-Aware Representation Learning

no code implementations27 Jan 2023 Jie Zhu, Jiyang Qi, Mingyu Ding, Xiaokang Chen, Ping Luo, Xinggang Wang, Wenyu Liu, Leye Wang, Jingdong Wang

The study is mainly motivated by that random views, used in contrastive learning, and random masked (visible) patches, used in masked image modeling, are often about object parts.

A Unified Knowledge Graph Service for Developing Domain Language Models in AI Software

1 code implementation10 Dec 2022 Ruiqing Ding, Xiao Han, Leye Wang

By enhancing the task-specific training procedure with domain knowledge graphs, we propose KnowledgeDA, a unified and low-code domain language model development service.

Knowledge Graphs Language Modelling

Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment

1 code implementation11 Aug 2022 Jie Zhu, Leye Wang, Xiao Han

By simulating the attack mechanism as the safety test, SafeCompress can automatically compress a big model to a small one following the dynamic sparse training paradigm.

Inference Attack Membership Inference Attack +1

Secure Forward Aggregation for Vertical Federated Neural Networks

no code implementations28 Jun 2022 Shuowei Cai, Di Chai, Liu Yang, Junxue Zhang, Yilun Jin, Leye Wang, Kun Guo, Kai Chen

In this paper, we focus on SplitNN, a well-known neural network framework in VFL, and identify a trade-off between data security and model performance in SplitNN.

Federated Learning Privacy Preserving

Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks

no code implementations28 May 2022 Xiao Han, Leye Wang, Junjie Wu, Yuncong Yang

Basically, we propose to perturb the original network by adding or removing links, and expect the embedding generated on the perturbed network can leak little information about private links but hold high utility for various downstream tasks.

Network Embedding Privacy Preserving

The Principle of Least Sensing: A Privacy-Friendly Sensing Paradigm for Urban Big Data Analytics

no code implementations11 Apr 2022 Leye Wang

With the worldwide emergence of data protection regulations, how to conduct law-regulated big data analytics becomes a challenging and fundamental problem.

Data Valuation for Vertical Federated Learning: An Information-Theoretic Approach

no code implementations15 Dec 2021 Xiao Han, Leye Wang, Junjie Wu

Federated learning (FL) is a promising machine learning paradigm that enables cross-party data collaboration for real-world AI applications in a privacy-preserving and law-regulated way.

Federated Learning Privacy Preserving

ES-CRF: Embedded Superpixel CRF for Semantic Segmentation

no code implementations14 Dec 2021 Jie Zhu, Huabin Huang, Banghuai Li, Leye Wang

It utilizes CRF to guide the message passing between pixels in high-level features to purify the feature representation of boundary pixels, with the help of inner pixels belong to the same object.

Metric Learning Model Optimization +1

MSP : Refine Boundary Segmentation via Multiscale Superpixel

no code implementations3 Dec 2021 Jie Zhu, Huabin Huang, Banghuai Li, Yong liu, Leye Wang

Inspired by the generated sharp edges of superpixel blocks, we employ superpixel to guide the information passing within feature map.

Scene Parsing Semantic Segmentation

DistFL: Distribution-aware Federated Learning for Mobile Scenarios

1 code implementation22 Oct 2021 Bingyan Liu, Yifeng Cai, Ziqi Zhang, Yuanchun Li, Leye Wang, Ding Li, Yao Guo, Xiangqun Chen

Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models.

Federated Learning Privacy Preserving

Practical and Secure Federated Recommendation with Personalized Masks

no code implementations18 Aug 2021 Liu Yang, Junxue Zhang, Di Chai, Leye Wang, Kun Guo, Kai Chen, Qiang Yang

In this paper, we proposed federated masked matrix factorization (FedMMF) to protect the data privacy in federated recommender systems without sacrificing efficiency and effectiveness.

Federated Learning Recommendation Systems

Benchmarking Contextual Factor Generalizability in Spatiotemporal Crowd Flow Prediction

1 code implementation30 Jun 2021 Liyue Chen, Leye Wang

In this paper, we develop an experimental platform composed of large-scale spatiotemporal crowd flow data, contextual data, and state-of-the-art spatiotemporal prediction models to conduct a comprehensive experimental study to quantitatively investigate the generalizability of different contextual features and modeling techniques in three urban crowd flow prediction scenarios (bike flow, metro passenger flow, and electric vehicle charging demand).

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

FedEval: A Holistic Evaluation Framework for Federated Learning

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

In this paper, we propose a holistic evaluation framework for FL called FedEval, and present a benchmarking study on seven state-of-the-art FL algorithms.

Federated Learning Privacy Preserving

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 Traffic Prediction: Meta-Modeling and an Analytic Framework

1 code implementation20 Sep 2020 Leye Wang, Di Chai, Xuanzhe Liu, Liyue Chen, Kai Chen

The Spatio-Temporal Traffic Prediction (STTP) problem is a classical problem with plenty of prior research efforts that benefit from traditional statistical learning and recent deep learning approaches.

Traffic Prediction

CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction

no code implementations25 Sep 2019 Xu Geng, Lingyu Zhang, Shulin Li, Yuanbo Zhang, Lulu Zhang, Leye Wang, Qiang Yang, Hongtu Zhu, Jieping Ye

Deep learning based approaches have been widely used in various urban spatio-temporal forecasting problems, but most of them fail to account for the unsmoothness issue of urban data in their architecture design, which significantly deteriorates their prediction performance.

Graph Attention Spatio-Temporal Forecasting +1

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.

BIG-bench Machine Learning 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.

Management Transfer Learning

Bike Flow Prediction with Multi-Graph Convolutional Networks

1 code implementation28 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.

reinforcement-learning reinforcement Learning +1

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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