Search Results for author: Xue Li

Found 30 papers, 4 papers with code

Secure Control of Networked Control Systems Using Dynamic Watermarking

no code implementations26 Aug 2021 Dajun Du, Changda Zhang, Xue Li, Minrui Fei, Taicheng Yang, Huiyu Zhou

We here investigate secure control of networked control systems developing a new dynamic watermarking (DW) scheme.

An ensemble solver for segregated cardiovascular FSI

1 code implementation22 Jan 2021 Xue Li, Daniele E. Schiavazzi

Computational models are increasingly used for diagnosis and treatment of cardiovascular disease.

Computational Engineering, Finance, and Science Numerical Analysis Numerical Analysis

The Tsinghua University-Ma Huateng Telescopes for Survey: Overview and Performance of the System

no code implementations21 Dec 2020 Ji-Cheng Zhang, Xiao-Feng Wang, Jun Mo, Gao-Bo Xi, Jie Lin, Xiao-Jun Jiang, Xiao-Ming Zhang, Wen-Xiong Li, Sheng-Yu Yan, Zhi-Hao Chen, Lei Hu, Xue Li, Wei-Li Lin, Han Lin, Cheng Miao, Li-Ming Rui, Han-Na Sai, Dan-Feng Xiang, Xing-Han Zhang

The TMTS system can have a FoV of about 9 deg2 when monitoring the sky with two bands (i. e., SDSS g and r filters) at the same time, and a maximum FoV of ~18 deg2 when four telescopes monitor different sky areas in monochromatic filter mode.

Instrumentation and Methods for Astrophysics

Learning Causal Bayesian Networks from Text

no code implementations ALTA 2020 Farhad Moghimifar, Afshin Rahimi, Mahsa Baktashmotlagh, Xue Li

Causal relationships form the basis for reasoning and decision-making in Artificial Intelligence systems.

Decision Making

A Unified Model for Recommendation with Selective Neighborhood Modeling

no code implementations19 Oct 2020 Jingwei Ma, Jiahui Wen, Panpan Zhang, Guangda Zhang, Xue Li

To address this issue, we propose a novel neighborhood-based recommender, where a hybrid gated network is designed to automatically separate similar neighbors from dissimilar (noisy) ones, and aggregate those similar neighbors to comprise neighborhood representations.

Collaborative Filtering

Understanding the Message Passing in Graph Neural Networks via Power Iteration Clustering

1 code implementation30 May 2020 Xue Li, Yuanzhi Cheng

The mechanism of message passing in graph neural networks (GNNs) is still mysterious.

Learning Various Length Dependence by Dual Recurrent Neural Networks

no code implementations28 May 2020 Chenpeng Zhang, Shuai Li, Mao Ye, Ce Zhu, Xue Li

Many variants of RNN have been proposed to solve the gradient problems of training RNNs and process long sequences.

Disentanglement Then Reconstruction: Learning Compact Features for Unsupervised Domain Adaptation

no code implementations28 May 2020 Lihua Zhou, Mao Ye, Xinpeng Li, Ce Zhu, Yiguang Liu, Xue Li

By this reconstructor, we can construct prototypes for the original features using class prototypes and domain prototypes correspondingly.

Unsupervised Domain Adaptation

Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks

no code implementations16 Apr 2020 Shaoxiong Ji, Xue Li, Zi Huang, Erik Cambria

Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment.

Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning

no code implementations21 Mar 2020 Shaoxiong Ji, Wenqi Jiang, Anwar Walid, Xue Li

Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization.

Federated Learning Image Classification +1

Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking

no code implementations19 Mar 2020 Hanlin Zhu, Xue Li, Liuyang Sun, Fei He, Zhengtuo Zhao, Lan Luan, Ngoc Mai Tran, Chong Xie

Across many areas, from neural tracking to database entity resolution, manual assessment of clusters by human experts presents a bottleneck in rapid development of scalable and specialized clustering methods.

Entity Resolution Model Selection +1

Medi-Care AI: Predicting Medications From Billing Codes via Robust Recurrent Neural Networks

no code implementations14 Nov 2019 Deyin Liu, Lin Wu, Xue Li

In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence of diagnostic billing codes in their record.

Sequence-Aware Factorization Machines for Temporal Predictive Analytics

no code implementations7 Nov 2019 Tong Chen, Hongzhi Yin, Quoc Viet Hung Nguyen, Wen-Chih Peng, Xue Li, Xiaofang Zhou

As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics.

Recommendation Systems

DBRec: Dual-Bridging Recommendation via Discovering Latent Groups

no code implementations27 Sep 2019 Jingwei Ma, Jiahui Wen, Mingyang Zhong, Liangchen Liu, Chaojie Li, Weitong Chen, Yin Yang, Honghui Tu, Xue Li

In addition, we propose to jointly learn user-user group (item-item group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations.

Collaborative Filtering Recommendation Systems

Learning Fast Matching Models from Weak Annotations

no code implementations30 Jan 2019 Xue Li, Zhipeng Luo, Hao Sun, Jianjin Zhang, Weihao Han, Xianqi Chu, Liangjie Zhang, Qi Zhang

The proposed training framework targets on mitigating both issues, by treating the stronger but undeployable models as annotators, and learning a deployable model from both human provided relevance labels and weakly annotated search log data.

Learning Private Neural Language Modeling with Attentive Aggregation

4 code implementations17 Dec 2018 Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, Zi Huang

Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestion by training models in distributed clients rather than training in a central server.

Federated Learning Language Modelling

Where-and-When to Look: Deep Siamese Attention Networks for Video-based Person Re-identification

no code implementations3 Aug 2018 Lin Wu, Yang Wang, Junbin Gao, Xue Li

Video-based person re-identification (re-id) is a central application in surveillance systems with significant concern in security.

Metric Learning Video-Based Person Re-Identification

YH Technologies at ActivityNet Challenge 2018

no code implementations29 Jun 2018 Ting Yao, Xue Li

This notebook paper presents an overview and comparative analysis of our systems designed for the following five tasks in ActivityNet Challenge 2018: temporal action proposals, temporal action localization, dense-captioning events in videos, trimmed action recognition, and spatio-temporal action localization.

Action Recognition Spatio-Temporal Action Localization

The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing

no code implementations8 May 2018 Zheng Xu, Xitong Yang, Xue Li, Xiaoshuai Sun

We propose a novel deep neural network architecture for the challenging problem of single image dehazing, which aims to recover the clear image from a degraded hazy image.

Image Dehazing Single Image Dehazing

When Point Process Meets RNNs: Predicting Fine-Grained User Interests with Mutual Behavioral Infectivity

no code implementations14 Oct 2017 Tong Chen, Lin Wu, Yang Wang, Jun Zhang, Hongxu Chen, Xue Li

Inspired by point process in modeling temporal point process, in this paper we present a deep prediction method based on two recurrent neural networks (RNNs) to jointly model each user's continuous browsing history and asynchronous event sequences in the context of inter-user behavioral mutual infectivity.

What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification

no code implementations21 Jul 2017 Lin Wu, Yang Wang, Xue Li, Junbin Gao

To address \emph{what} to match, our deep network emphasizes common local patterns by learning joint representations in a multiplicative way.

Person Re-Identification

PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking

no code implementations17 Jul 2017 Meng Wang, Jiaheng Zhang, Jun Liu, Wei Hu, Sen Wang, Xue Li, Wenqiang Liu

Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge.

Entity Linking Knowledge Graphs

Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification

no code implementations10 Jun 2017 Lin Wu, Yang Wang, Junbin Gao, Xue Li

To this end, a novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep embedding.

Metric Learning Person Re-Identification

Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection

no code implementations20 Apr 2017 Tong Chen, Lin Wu, Xue Li, Jun Zhang, Hongzhi Yin, Yang Wang

The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time.

Deep Attention

Uncovering Locally Discriminative Structure for Feature Analysis

no code implementations9 Jul 2016 Sen Wang, Feiping Nie, Xiaojun Chang, Xue Li, Quan Z. Sheng, Lina Yao

We propose a method that utilizes both the manifold structure of data and local discriminant information.

Unsupervised Feature Analysis with Class Margin Optimization

no code implementations3 Jun 2015 Sen Wang, Feiping Nie, Xiaojun Chang, Lina Yao, Xue Li, Quan Z. Sheng

In this paper, we propose an unsupervised feature selection method seeking a feature coefficient matrix to select the most distinctive features.

Feature Selection

Image Tag Completion by Low-rank Factorization with Dual Reconstruction Structure Preserved

no code implementations9 Jun 2014 Xue Li, Yu-Jin Zhang, Bin Shen, Bao-Di Liu

A novel tag completion algorithm is proposed in this paper, which is designed with the following features: 1) Low-rank and error s-parsity: the incomplete initial tagging matrix D is decomposed into the complete tagging matrix A and a sparse error matrix E. However, instead of minimizing its nuclear norm, A is further factor-ized into a basis matrix U and a sparse coefficient matrix V, i. e. D=UV+E.

Denoising

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