Search Results for author: Jun Yan

Found 27 papers, 8 papers with code

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 +1

On the Robustness of Reading Comprehension Models to Entity Renaming

no code implementations16 Oct 2021 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 answer entities have different names?

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

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

Suicide Risk Modeling with Uncertain Diagnostic Records

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 +5

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 +1

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

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.

Methodology

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.

Computation

LINE: Large-scale Information Network Embedding

9 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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