Search Results for author: Jun Yan

Found 39 papers, 12 papers with code

DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness

no code implementations7 Jun 2023 Jianpeng Liao, Jun Yan, Qian Tao

The DualHGNN first leverages a multi-view hypergraph learning network to explore the optimal hypergraph structure from multiple views, constrained by a consistency loss proposed to improve its generalization.

MULTI-VIEW LEARNING Node Classification +1

Low-discrepancy Sampling in the Expanded Dimensional Space: An Acceleration Technique for Particle Swarm Optimization

no code implementations6 Mar 2023 Feng Wu, Yuelin Zhao, Jianhua Pang, Jun Yan, Wanxie Zhong

The acceleration technique can generate a low-discrepancy sample set with a smaller dispersion, compared with a random sampling, in the expanded dimensional space; it also reduces the error at each iteration, and hence improves the convergence speed.

Locating the Sources of Sub-synchronous Oscillations Induced by the Control of Voltage Source Converters Based on Energy Structure and Nonlinearity Detection

no code implementations11 Feb 2023 Zetian Zheng, Shaowei Huang, Jun Yan, Qiangsheng Bu, Chen Shen, Mingzhong Zheng, Ye Liu

The oscillation phenomena associated with the control of voltage source converters (VSCs) are widely concerning, and locating the source of these oscillations is crucial to suppressing them; therefore, this paper presents a locating scheme, based on the energy structure and nonlinearity detection.

Distributed Interaction Graph Construction for Dynamic DCOPs in Cooperative Multi-agent Systems

no code implementations7 Dec 2022 Brighter Agyemang, Fenghui Ren, Jun Yan

In open and dynamic environments, such methods need to address how this interaction graph is generated and maintained among agents.

graph construction

Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network

no code implementations25 Oct 2022 Huan Hua, Jun Yan, Xi Fang, Weiquan Huang, Huilin Yin, Wancheng Ge

With the utilization of such a framework, the influence of non-robust features could be mitigated to strengthen the adversarial robustness.

Adversarial Robustness Causal Inference

FedSSO: A Federated Server-Side Second-Order Optimization Algorithm

no code implementations20 Jun 2022 Xin Ma, Renyi Bao, Jinpeng Jiang, Yang Liu, Arthur Jiang, Jun Yan, Xin Liu, Zhisong Pan

In this work, we propose FedSSO, a server-side second-order optimization method for federated learning (FL).

Federated Learning

Wavelet Regularization Benefits Adversarial Training

1 code implementation8 Jun 2022 Jun Yan, Huilin Yin, Xiaoyang Deng, Ziming Zhao, Wancheng Ge, Hao Zhang, Gerhard Rigoll

Since adversarial vulnerability can be regarded as a high-frequency phenomenon, it is essential to regulate the adversarially-trained neural network models in the frequency domain.

Adversarial Robustness

BITE: Textual Backdoor Attacks with Iterative Trigger Injection

1 code implementation25 May 2022 Jun Yan, Vansh Gupta, Xiang Ren

We propose BITE, a backdoor attack that poisons the training data to establish strong correlations between the target label and a set of "trigger words".

Backdoor Attack Hate Speech Detection +3

Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification

no code implementations27 Jan 2022 Jianpeng Liao, Qian Tao, Jun Yan

Graph-based semi-supervised learning, which can exploit the connectivity relationship between labeled and unlabeled data, has been shown to outperform the state-of-the-art in many artificial intelligence applications.

Classification Graph Attention +1

Pricing Time-to-Event Contingent Cash Flows: A Discrete-Time Survival Analysis Approach

no code implementations13 Jan 2022 Jackson P. Lautier, Vladimir Pozdnyakov, Jun Yan

In an application to a subset of 29, 845 36-month leases from the Mercedes-Benz Auto Lease Trust 2017-A (MBALT 2017-A) bond, our pricing model yields estimates closer to the actual realized future cash flows than the non-random time-to-event model, especially as the fitting window increases.

Survival Analysis

Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic

no code implementations11 Nov 2021 Wei Zhou, Dong Chen, Jun Yan, Zhaojian Li, Huilin Yin, Wanchen Ge

In this paper, we formulate the lane-changing decision making of multiple AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning (MARL) problem, where each AV makes lane-changing decisions based on the motions of both neighboring AVs and HDVs.

Autonomous Driving Decision Making +3

On the Robustness of Reading Comprehension Models to Entity Renaming

1 code implementation NAACL 2022 Jun Yan, Yang Xiao, Sagnik Mukherjee, Bill Yuchen Lin, Robin Jia, Xiang Ren

We study the robustness of machine reading comprehension (MRC) models to entity renaming -- do models make more wrong predictions when the same questions are asked about an entity whose name has been changed?

Continual Pretraining Machine Reading Comprehension

On Procedural Adversarial Noise Attack And Defense

1 code implementation10 Aug 2021 Jun Yan, Xiaoyang Deng, Huilin Yin, Wancheng Ge

Deep Neural Networks (DNNs) are vulnerable to adversarial examples which would inveigle neural networks to make prediction errors with small perturbations on the input images.

Monitoring Object Detection Abnormalities via Data-Label and Post-Algorithm Abstractions

no code implementations29 Mar 2021 Yuhang Chen, Chih-Hong Cheng, Jun Yan, Rongjie Yan

While object detection modules are essential functionalities for any autonomous vehicle, the performance of such modules that are implemented using deep neural networks can be, in many cases, unreliable.

object-detection Object Detection

Regional and Sectoral Structures and Their Dynamics of Chinese Economy: A Network Perspective from Multi-Regional Input-Output Tables

no code implementations24 Feb 2021 Tao Wang, Shiying Xiao, Jun Yan, Panpan Zhang

Quantified metrics assessing the relative importance of the province-sectors in the national economy echo the national and regional economic development policies to a certain extent.

Community Detection Physics and Society General Economics Economics Applications

Learning Contextualized Knowledge Graph Structures for Commonsense Reasoning

no code implementations1 Jan 2021 Jun Yan, Mrigank Raman, Tianyu Zhang, Ryan Rossi, Handong Zhao, Sungchul Kim, Nedim Lipka, Xiang Ren

Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense) question answering and natural language inference.

Knowledge Graphs Natural Language Inference +1

Survival Modeling of Suicide Risk with Rare and Uncertain Diagnoses

no code implementations5 Sep 2020 Wenjie Wang, Chongliang Luo, Robert H. Aseltine, Fei Wang, Jun Yan, Kun Chen

Motivated by the pressing need for suicide prevention through improving behavioral healthcare, we use medical claims data to study the risk of subsequent suicide attempts for patients who were hospitalized due to suicide attempts and later discharged.

Survival Analysis

Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering

2 code implementations EMNLP 2020 Yanlin Feng, Xinyue Chen, Bill Yuchen Lin, Peifeng Wang, Jun Yan, Xiang Ren

Existing work on augmenting question answering (QA) models with external knowledge (e. g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model's prediction rationale.

Knowledge Graphs Question Answering +1

Computational Performance of a Germline Variant Calling Pipeline for Next Generation Sequencing

no code implementations1 Apr 2020 Jie Liu, Xiaotian Wu, Kai Zhang, Bing Liu, Renyi Bao, Xiao Chen, Yiran Cai, Yiming Shen, Xinjun He, Jun Yan, Weixing Ji

With the booming of next generation sequencing technology and its implementation in clinical practice and life science research, the need for faster and more efficient data analysis methods becomes pressing in the field of sequencing.

Probing new physics with multi-vacua quantum tunnelings beyond standard model through gravitational waves

no code implementations30 Mar 2020 Zihan Zhou, Jun Yan, Andrea Addazi, Yi-Fu Cai, Antonino Marciano, Roman Pasechnik

We report on a novel phenomenon of particle cosmology, which features specific cosmological phase transitions via quantum tunnelings through multiple vacua.

Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology High Energy Physics - Theory

Learning from Explanations with Neural Execution Tree

1 code implementation ICLR 2020 Ziqi Wang, Yujia Qin, Wenxuan Zhou, Jun Yan, Qinyuan Ye, Leonardo Neves, Zhiyuan Liu, Xiang Ren

While deep neural networks have achieved impressive performance on a range of NLP tasks, these data-hungry models heavily rely on labeled data, which restricts their applications in scenarios where data annotation is expensive.

Data Augmentation Multi-hop Question Answering +6

A Deep Learning-Based System for PharmaCoNER

no code implementations WS 2019 Ying Xiong, Yedan Shen, Yuanhang Huang, Shuai Chen, Buzhou Tang, Xiaolong Wang, Qingcai Chen, Jun Yan, Yi Zhou

The Biological Text Mining Unit at BSC and CNIO organized the first shared task on chemical {\&} drug mention recognition from Spanish medical texts called PharmaCoNER (Pharmacological Substances, Compounds and proteins and Named Entity Recognition track) in 2019, which includes two tracks: one for NER offset and entity classification (track 1) and the other one for concept indexing (track 2).

General Classification named-entity-recognition +2

HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets

no code implementations WS 2019 Shuai Chen, Yuanhang Huang, Xiaowei Huang, Haoming Qin, Jun Yan, Buzhou Tang

This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fourth Social Media Mining for Health Applications (SMM4H) shared task in 2019.

Learning Dual Retrieval Module for Semi-supervised Relation Extraction

1 code implementation20 Feb 2019 Hongtao Lin, Jun Yan, Meng Qu, Xiang Ren

In this paper, we leverage a key insight that retrieving sentences expressing a relation is a dual task of predicting relation label for a given sentence---two tasks are complementary to each other and can be optimized jointly for mutual enhancement.

MULTI-VIEW LEARNING Relation Extraction +1

Language Modeling with Sparse Product of Sememe Experts

1 code implementation EMNLP 2018 Yihong Gu, Jun Yan, Hao Zhu, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Fen Lin, Leyu Lin

Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words.

Language Modelling

An Online Updating Approach for Testing the Proportional Hazards Assumption with Streams of Big Survival Data

no code implementations5 Sep 2018 Yishu Xue, Haiying Wang, Jun Yan, Elizabeth D. Schifano

The Cox model, which remains as the first choice in analyzing time-to-event data even for large datasets, relies on the proportional hazards assumption.


Atomic Norm Based Localization of Far-Field and Near-Field Signals with Generalized Symmetric Arrays

no code implementations5 Dec 2017 Xiaohuan Wu, Wei-Ping Zhu, Jun Yan

Most localization methods for mixed far-field (FF) and near-field (NF) sources are based on uniform linear array (ULA) rather than sparse linear array (SLA).

Super-Resolution Information Theory Signal Processing Information Theory

Active Sentiment Domain Adaptation

no code implementations ACL 2017 Fangzhao Wu, Yongfeng Huang, Jun Yan

Instead of the source domain sentiment classifiers, our approach adapts the general-purpose sentiment lexicons to target domain with the help of a small number of labeled samples which are selected and annotated in an active learning mode, as well as the domain-specific sentiment similarities among words mined from unlabeled samples of target domain.

Active Learning Domain Adaptation +2

Wilcoxon Rank-Based Tests for Clustered Data with R Package clusrank

no code implementations11 Jun 2017 Yujing Jiang, Xin He, Mei-Ling Ting Lee, Bernard Rosner, Jun Yan

For independent data, they are available in several R packages such as stats and coin.


LINE: Large-scale Information Network Embedding

8 code implementations12 Mar 2015 Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.

Graph Embedding Link Prediction +2

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