no code implementations • 31 Aug 2024 • Jun Yan, Wenjie Jacky Mo, Xiang Ren, Robin Jia
Backdoor detection methods aim to detect whether a released model contains a backdoor, so that practitioners can avoid such vulnerabilities.
no code implementations • 19 Aug 2024 • Chengming Hu, Hao Zhou, Di wu, Xi Chen, Jun Yan, Xue Liu
Following this comprehensive feedback, our proposed TrafficLLM introduces refinement demonstration prompts, enabling the same LLM to further refine its predictions and thereby enhance prediction performance.
1 code implementation • 19 Aug 2024 • Jun Yan, Pengyu Wang, Danni Wang, Weiquan Huang, Daniel Watzenig, Huilin Yin
In the task of semantic segmentation for autonomous driving, it is significant to study the zero-shot adversarial robustness of SAM.
no code implementations • 24 May 2024 • Yuanliang Li, Hanzheng Dai, Jun Yan
In our study, we specifically focus on lateral movement as a PT case study and formulate it as a partially observable Markov decision process (POMDP) guided by RMs.
no code implementations • 25 Apr 2024 • Xiaohong Liu, Xiongkuo Min, Guangtao Zhai, Chunyi Li, Tengchuan Kou, Wei Sun, HaoNing Wu, Yixuan Gao, Yuqin Cao, ZiCheng Zhang, Xiele Wu, Radu Timofte, Fei Peng, Huiyuan Fu, Anlong Ming, Chuanming Wang, Huadong Ma, Shuai He, Zifei Dou, Shu Chen, Huacong Zhang, Haiyi Xie, Chengwei Wang, Baoying Chen, Jishen Zeng, Jianquan Yang, Weigang Wang, Xi Fang, Xiaoxin Lv, Jun Yan, Tianwu Zhi, Yabin Zhang, Yaohui Li, Yang Li, Jingwen Xu, Jianzhao Liu, Yiting Liao, Junlin Li, Zihao Yu, Yiting Lu, Xin Li, Hossein Motamednia, S. Farhad Hosseini-Benvidi, Fengbin Guan, Ahmad Mahmoudi-Aznaveh, Azadeh Mansouri, Ganzorig Gankhuyag, Kihwan Yoon, Yifang Xu, Haotian Fan, Fangyuan Kong, Shiling Zhao, Weifeng Dong, Haibing Yin, Li Zhu, Zhiling Wang, Bingchen Huang, Avinab Saha, Sandeep Mishra, Shashank Gupta, Rajesh Sureddi, Oindrila Saha, Luigi Celona, Simone Bianco, Paolo Napoletano, Raimondo Schettini, Junfeng Yang, Jing Fu, Wei zhang, Wenzhi Cao, Limei Liu, Han Peng, Weijun Yuan, Zhan Li, Yihang Cheng, Yifan Deng, Haohui Li, Bowen Qu, Yao Li, Shuqing Luo, Shunzhou Wang, Wei Gao, Zihao Lu, Marcos V. Conde, Xinrui Wang, Zhibo Chen, Ruling Liao, Yan Ye, Qiulin Wang, Bing Li, Zhaokun Zhou, Miao Geng, Rui Chen, Xin Tao, Xiaoyu Liang, Shangkun Sun, Xingyuan Ma, Jiaze Li, Mengduo Yang, Haoran Xu, Jie zhou, Shiding Zhu, Bohan Yu, Pengfei Chen, Xinrui Xu, Jiabin Shen, Zhichao Duan, Erfan Asadi, Jiahe Liu, Qi Yan, Youran Qu, Xiaohui Zeng, Lele Wang, Renjie Liao
A total of 196 participants have registered in the video track.
no code implementations • 20 Apr 2024 • Xi Fang, Weigang Wang, Xiaoxin Lv, Jun Yan
It is essential to build an effective quality assessment framework to provide a quantifiable evaluation of different images or videos based on the AIGC technologies.
1 code implementation • 19 Apr 2024 • Huilin Yin, Jiaxiang Li, Pengju Zhen, Jun Yan
This method searches the sensitive region of trajectory prediction models and generates the adversarial trajectories by using the vehicle-following method and incorporating information about forthcoming trajectories.
no code implementations • 28 Feb 2024 • Luyang Hou, Jun Yan, Yuankai Wu, Chun Wang, Tie Qiu
Energy Internet (EI) is emerging as new share economy platform for flexible local energy supplies in smart cities.
1 code implementation • 18 Feb 2024 • Kai Chen, Zihao He, Jun Yan, Taiwei Shi, Kristina Lerman
Large Language Models (LLMs) possess the potential to exert substantial influence on public perceptions and interactions with information.
no code implementations • 27 Dec 2023 • Jiangkun Gong, Jun Yan, Deyong Kong, Deren Li
In this study, we showcased the detection of the wake vortex produced by a medium aircraft at distances exceeding 10 km using an X-band pulse-Doppler radar.
no code implementations • 22 Dec 2023 • Chengming Hu, Haolun Wu, Xuan Li, Chen Ma, Xi Chen, Jun Yan, Boyu Wang, Xue Liu
A simple neural network then learns the implicit mapping from the intra- and inter-sample relations to an adaptive, sample-wise knowledge fusion ratio in a bilevel-optimization manner.
no code implementations • 16 Nov 2023 • Wenjie Mo, Jiashu Xu, Qin Liu, Jiongxiao Wang, Jun Yan, Chaowei Xiao, Muhao Chen
Existing studies in backdoor defense have predominantly focused on the training phase, overlooking the critical aspect of testing time defense.
no code implementations • 2 Nov 2023 • Xinlu Zhang, Yujie Lu, Weizhi Wang, An Yan, Jun Yan, Lianke Qin, Heng Wang, Xifeng Yan, William Yang Wang, Linda Ruth Petzold
Automatically evaluating vision-language tasks is challenging, especially when it comes to reflecting human judgments due to limitations in accounting for fine-grained details.
no code implementations • 23 Oct 2023 • Jiangkun Gong, Jun Yan, Deyong Kong, Deren Li
This paper presents a brief examination of Automatic Target Recognition (ATR) technology within ground-based radar systems.
no code implementations • 27 Sep 2023 • Jiangkun Gong, Jun Yan, Deyong Kong, Ruizhi Chen, Deren Li
Radar echoes from bird flocks contain modulation signals, which we find are produced by the flapping gaits of birds in the flock, resulting in a group of spectral peaks with similar amplitudes spaced at a specific interval.
1 code implementation • 31 Jul 2023 • Jun Yan, Vikas Yadav, Shiyang Li, Lichang Chen, Zheng Tang, Hai Wang, Vijay Srinivasan, Xiang Ren, Hongxia Jin
To demonstrate the threat, we propose a simple method to perform VPI by poisoning the model's instruction tuning data, which proves highly effective in steering the LLM.
no code implementations • 20 Jul 2023 • Shiyang Li, Jun Yan, Hai Wang, Zheng Tang, Xiang Ren, Vijay Srinivasan, Hongxia Jin
We conduct a comprehensive evaluation of four major model families across nine datasets, employing twelve sets of verbalizers for each of them.
no code implementations • 19 Jul 2023 • Jiangkun Gong, Jun Yan, Deyong Kong, Deren Li
The authors suggest integrating ATR capabilities into drone detection radar systems to improve performance and manage emerging threats.
3 code implementations • 17 Jul 2023 • Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, Hongxia Jin
Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data.
no code implementations • 7 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.
no code implementations • 6 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.
no code implementations • 11 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.
no code implementations • 7 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.
no code implementations • 16 Nov 2022 • Jackson P. Lautier, Vladimir Pozdnyakov, Jun Yan
Loan seasoning and inefficient consumer interest rate refinance behavior are well-known for mortgages.
no code implementations • 25 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.
no code implementations • 15 Sep 2022 • Zeyu Fu, Zhuang Fu, Chenzhuo Lu, Jun Yan, Jian Fei, Hui Han
Based on TV-TRPCA, the accuracy of TSRG algorithm for vessel segmentation is further evaluated.
no code implementations • 20 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).
1 code implementation • 8 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.
1 code implementation • 25 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".
no code implementations • 27 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.
no code implementations • 13 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.
no code implementations • 11 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.
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?
1 code implementation • EMNLP 2021 • Bill Yuchen Lin, Wenyang Gao, Jun Yan, Ryan Moreno, Xiang Ren
To audit the robustness of named entity recognition (NER) models, we propose RockNER, a simple yet effective method to create natural adversarial examples.
1 code implementation • 10 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.
2 code implementations • ACL 2022 • Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei LI, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on Semantic Similarity on CHIP-STS
no code implementations • ACL 2021 • Jun Yan, Nasser Zalmout, Yan Liang, Christan Grant, Xiang Ren, Xin Luna Dong
However, this approach constrains knowledge sharing across different attributes.
no code implementations • 29 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.
no code implementations • 24 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
no code implementations • 1 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.
1 code implementation • Findings (ACL) 2021 • Jun Yan, Mrigank Raman, Aaron Chan, Tianyu Zhang, Ryan Rossi, Handong Zhao, Sungchul Kim, Nedim Lipka, Xiang Ren
Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks.
no code implementations • 5 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.
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.
no code implementations • 1 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.
no code implementations • 30 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
no code implementations • 5 Nov 2019 • Andrea Montanari, Feng Ruan, Youngtak Sohn, Jun Yan
They achieve this by learning nonlinear representations of the inputs that maps the data into linearly separable classes.
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.
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).
no code implementations • 2 Sep 2019 • Linfeng Li, Peng Wang, Yao Wang, Jinpeng Jiang, Buzhou Tang, Jun Yan, Sheng-Hui Wang, Yu-Ting Liu
This paper proposes an algorithm named as PrTransH to learn embedding vectors from real world EMR data based medical knowledge.
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.
1 code implementation • 20 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.
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.
no code implementations • 5 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
no code implementations • 5 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
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.
no code implementations • 11 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
8 code implementations • 12 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.
Ranked #4 on Node Classification on Eximtradedata