Search Results for author: Chao Wu

Found 18 papers, 6 papers with code

Federated Graph Learning -- A Position Paper

no code implementations24 May 2021 Huanding Zhang, Tao Shen, Fei Wu, Mingyang Yin, Hongxia Yang, Chao Wu

Federated learning (FL) is a an emerging technique that can collaboratively train a shared model while keeping the data decentralized, which is a rational solution for distributed GNN training.

Federated Learning Graph Learning

Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent

no code implementations15 Apr 2021 Zhao Wang, Yifan Hu, Jun Xiao, Chao Wu

A novel ring FL topology as well as a map-reduce based synchronizing method are designed in the proposed RDFL to improve decentralized FL performance and bandwidth utilization.

Federated Learning

Verifying Design through Generative Visualization of Neural Activities

no code implementations28 Mar 2021 Pan Wang, Danlin Peng, Simiao Yu, Chao Wu, Peter Childs, Yike Guo, Ling Li

A recurrent neural network is used as the encoder to learn latent representation from electroencephalogram (EEG) signals, recorded while subjects looked at 50 categories of images.

EEG

GFL: A Decentralized Federated Learning Framework Based On Blockchain

no code implementations21 Oct 2020 Yifan Hu, YuHang Zhou, Jun Xiao, Chao Wu

Federated learning(FL) is a rapidly growing field and many centralized and decentralized FL frameworks have been proposed.

Data Poisoning Federated Learning

Federated Unsupervised Representation Learning

no code implementations18 Oct 2020 Fengda Zhang, Kun Kuang, Zhaoyang You, Tao Shen, Jun Xiao, Yin Zhang, Chao Wu, Yueting Zhuang, Xiaolin Li

FURL poses two new challenges: (1) data distribution shift (Non-IID distribution) among clients would make local models focus on different categories, leading to the inconsistency of representation spaces.

Federated Learning Unsupervised Representation Learning

Federated Mutual Learning

1 code implementation27 Jun 2020 Tao Shen, Jie Zhang, Xinkang Jia, Fengda Zhang, Gang Huang, Pan Zhou, Kun Kuang, Fei Wu, Chao Wu

The experiments show that FML can achieve better performance than alternatives in typical FL setting, and clients can be benefited from FML with different models and tasks.

Federated Learning

Evaluation Framework For Large-scale Federated Learning

1 code implementation3 Mar 2020 Lifeng Liu, Fengda Zhang, Jun Xiao, Chao Wu

Federated learning is proposed as a machine learning setting to enable distributed edge devices, such as mobile phones, to collaboratively learn a shared prediction model while keeping all the training data on device, which can not only take full advantage of data distributed across millions of nodes to train a good model but also protect data privacy.

Federated Learning

Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach

no code implementations6 Feb 2020 Zeyue Xue, Shuang Luo, Chao Wu, Pan Zhou, Kaigui Bian, Wei Du

Peer-to-peer knowledge transfer in distributed environments has emerged as a promising method since it could accelerate learning and improve team-wide performance without relying on pre-trained teachers in deep reinforcement learning.

Model distillation Transfer Learning

Galaxy Learning -- A Position Paper

no code implementations22 Apr 2019 Chao Wu, Jun Xiao, Gang Huang, Fei Wu

Model training, as well as the communication, is achieved with blockchain and its smart contracts.

Deep Sequence Learning with Auxiliary Information for Traffic Prediction

1 code implementation13 Jun 2018 Binbing Liao, Jingqing Zhang, Chao Wu, Douglas McIlwraith, Tong Chen, Shengwen Yang, Yike Guo, Fei Wu

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved.

Traffic Prediction

Generative Creativity: Adversarial Learning for Bionic Design

no code implementations19 May 2018 Simiao Yu, Hao Dong, Pan Wang, Chao Wu, Yike Guo

Bionic design refers to an approach of generative creativity in which a target object (e. g. a floor lamp) is designed to contain features of biological source objects (e. g. flowers), resulting in creative biologically-inspired design.

Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security

no code implementations20 Nov 2017 Hao Dong, Chao Wu, Zhen Wei, Yike Guo

However, current architecture of deep networks suffers the privacy issue that users need to give out their data to the model (typically hosted in a server or a cluster on Cloud) for training or prediction.

Anomaly Detection Decision Making +1

Semantic Image Synthesis via Adversarial Learning

1 code implementation ICCV 2017 Hao Dong, Simiao Yu, Chao Wu, Yike Guo

In this paper, we propose a way of synthesizing realistic images directly with natural language description, which has many useful applications, e. g. intelligent image manipulation.

Image Generation Image Manipulation

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

7 code implementations12 Mar 2017 Akara Supratak, Hao Dong, Chao Wu, Yike Guo

This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different datasets without utilizing any hand-engineered features.

EEG Sleep Stage Detection

Unsupervised Image-to-Image Translation with Generative Adversarial Networks

no code implementations10 Jan 2017 Hao Dong, Paarth Neekhara, Chao Wu, Yike Guo

It's useful to automatically transform an image from its original form to some synthetic form (style, partial contents, etc.

Unsupervised Image-To-Image Translation

Mixed Neural Network Approach for Temporal Sleep Stage Classification

no code implementations15 Oct 2016 Hao Dong, Akara Supratak, Wei Pan, Chao Wu, Paul M. Matthews, Yike Guo

Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.

Classification EEG +1

Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge

no code implementations21 Sep 2015 Xiaofei Wang, Chao Wu, Pengyuan Zhang, Ziteng Wang, Yong liu, Xu Li, Qiang Fu, Yonghong Yan

This paper presents the contribution to the third 'CHiME' speech separation and recognition challenge including both front-end signal processing and back-end speech recognition.

automatic-speech-recognition Language Modelling +2

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