Search Results for author: Jun Wu

Found 29 papers, 6 papers with code

Adaptive Transfer Learning for Plant Phenotyping

no code implementations14 Jan 2022 Jun Wu, Elizabeth A. Ainsworth, Sheng Wang, Kaiyu Guan, Jingrui He

Plant phenotyping (Guo et al. 2021; Pieruschka et al. 2019) focuses on studying the diverse traits of plants related to the plants' growth.

GPR Transfer Learning

Learning Stereopsis from Geometric Synthesis for 6D Object Pose Estimation

no code implementations25 Sep 2021 Jun Wu, Lilu Liu, Yue Wang, Rong Xiong

Current monocular-based 6D object pose estimation methods generally achieve less competitive results than RGBD-based methods, mostly due to the lack of 3D information.

6D Pose Estimation using RGB

RF-LighGBM: A probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce

no code implementations2 Sep 2021 Liping Yang, Xiaxia Niu, Jun Wu

Given the complex problem of feature engineering, the classic model RFM in the field of customer relationship management is improved, and an improved model is proposed to describe the characteristics of customer buying behaviour, which includes five indicators.

Feature Engineering Hyperparameter Optimization

Automatically Lock Your Neural Networks When You're Away

no code implementations15 Mar 2021 Ge Ren, Jun Wu, Gaolei Li, Shenghong Li

The smartphone and laptop can be unlocked by face or fingerprint recognition, while neural networks which confront numerous requests every day have little capability to distinguish between untrustworthy and credible users.

A Universal Model for Cross Modality Mapping by Relational Reasoning

no code implementations26 Feb 2021 Zun Li, Congyan Lang, Liqian Liang, Tao Wang, Songhe Feng, Jun Wu, Yidong Li

With the aim of matching a pair of instances from two different modalities, cross modality mapping has attracted growing attention in the computer vision community.

Image Classification Relational Reasoning

Continuous Transfer Learning

no code implementations1 Jan 2021 Jun Wu, Jingrui He

One major challenge associated with continuous transfer learning is the time evolving relatedness of the source domain and the current target domain as the target domain evolves over time.

Transfer Learning

Robust Federated Learning for Neural Networks

no code implementations1 Jan 2021 Yao Zhou, Jun Wu, Jingrui He

In federated learning, data is distributed among local clients which collaboratively train a prediction model using secure aggregation.

Federated Learning

Leveraging AI and Intelligent Reflecting Surface for Energy-Efficient Communication in 6G IoT

no code implementations29 Dec 2020 Qianqian Pan, Jun Wu, Xi Zheng, Jianhua Li, Shenghong Li, Athanasios V. Vasilakos

The ever-increasing data traffic, various delay-sensitive services, and the massive deployment of energy-limited Internet of Things (IoT) devices have brought huge challenges to the current communication networks, motivating academia and industry to move to the sixth-generation (6G) network.

GAN-based Recommendation with Positive-Unlabeled Sampling

no code implementations12 Dec 2020 Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi Nasrabadi, Evren Korpeoglu, Kannan Achan, Jingrui He

Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products.

Information Retrieval Recommendation Systems

Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning

no code implementations18 Sep 2020 Yao Zhou, Jun Wu, Haixun Wang, Jingrui He

In this work, we show that this paradigm might inherit the adversarial vulnerability of the centralized neural network, i. e., it has deteriorated performance on adversarial examples when the model is deployed.

Adversarial Robustness Federated Learning

Structure Learning for Cyclic Linear Causal Models

no code implementations10 Jun 2020 Carlos Améndola, Philipp Dettling, Mathias Drton, Federica Onori, Jun Wu

We consider the problem of structure learning for linear causal models based on observational data.

Continuous Transfer Learning with Label-informed Distribution Alignment

no code implementations5 Jun 2020 Jun Wu, Jingrui He

To bridge this gap, in this paper, we study a novel continuous transfer learning setting with a time evolving target domain.

Transfer Learning

A framework for adaptive width control of dense contour-parallel toolpaths in fused deposition modeling

2 code implementations28 Apr 2020 Tim Kuipers, Eugeni L. Doubrovski, Jun Wu, Charlie C. L. Wang

In this paper we present a framework which supports multiple schemes to generate toolpaths with adaptive width, by employing a function to decide the number of beads and their widths.

Graphics Robotics Systems and Control Systems and Control J.6

Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks

no code implementations17 Apr 2020 Xin Chen, Lingxi Xie, Jun Wu, Longhui Wei, Yuhui Xu, Qi Tian

We alleviate this issue by training a graph convolutional network to fit the performance of sampled sub-networks so that the impact of random errors becomes minimal.

Neural Architecture Search

Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild

1 code implementation23 Dec 2019 Xin Chen, Lingxi Xie, Jun Wu, Qi Tian

With the rapid development of neural architecture search (NAS), researchers found powerful network architectures for a wide range of vision tasks.

Neural Architecture Search

Robust Data Preprocessing for Machine-Learning-Based Disk Failure Prediction in Cloud Production Environments

no code implementations20 Dec 2019 Shujie Han, Jun Wu, Erci Xu, Cheng He, Patrick P. C. Lee, Yi Qiang, Qixing Zheng, Tao Huang, Zixi Huang, Rui Li

To provide proactive fault tolerance for modern cloud data centers, extensive studies have proposed machine learning (ML) approaches to predict imminent disk failures for early remedy and evaluated their approaches directly on public datasets (e. g., Backblaze SMART logs).

Hierarchical Attention Networks for Medical Image Segmentation

no code implementations20 Nov 2019 Fei Ding, Gang Yang, Jinlu Liu, Jun Wu, Dayong Ding, Jie Xv, Gangwei Cheng, Xirong Li

Unlike previous self-attention based methods that capture context information from one level, we reformulate the self-attention mechanism from the view of the high-order graph and propose a novel method, namely Hierarchical Attention Network (HANet), to address the problem of medical image segmentation.

Medical Image Segmentation Semantic Segmentation

Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation

5 code implementations ICCV 2019 Xin Chen, Lingxi Xie, Jun Wu, Qi Tian

Recently, differentiable search methods have made major progress in reducing the computational costs of neural architecture search.

Neural Architecture Search

Autoencoder Based Residual Deep Networks for Robust Regression Prediction and Spatiotemporal Estimation

no code implementations29 Dec 2018 Lianfa Li, Ying Fang, Jun Wu, Jinfeng Wang

To have a superior generalization, a deep learning neural network often involves a large size of training sample.

Imputation

ImVerde: Vertex-Diminished Random Walk for Learning Network Representation from Imbalanced Data

1 code implementation24 Apr 2018 Jun Wu, Jingrui He, Yongming Liu

Then, based on VDRW, we propose a semi-supervised network representation learning framework named ImVerde for imbalanced networks, in which context sampling uses VDRW and the label information to create node-context pairs, and balanced-batch sampling adopts a simple under-sampling method to balance these pairs in different classes.

Social and Information Networks

Neural method for Explicit Mapping of Quasi-curvature Locally Linear Embedding in image retrieval

no code implementations11 Mar 2017 Shenglan Liu, Jun Wu, Lin Feng, Feilong Wang

This paper proposed a new explicit nonlinear dimensionality reduction using neural networks for image retrieval tasks.

Dimensionality Reduction Image Retrieval

Cannot find the paper you are looking for? You can Submit a new open access paper.