1 code implementation • ICML 2020 • Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Xiaodong Liu, Yu Wang, Jianfeng Gao, Songhao Piao, Ming Zhou, Hsiao-Wuen Hon
We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM).
no code implementations • CCL 2022 • Yu Wang, Yulin Yuan
“双重否定结构是一种“通过两次否定表示肯定意义”的特殊结构, 其存在会对自然语言处理中的语义判断与情感分类产生重要影响。本文以“eg eg P== extgreater P”为标准, 对现代汉语中所有的“否定词+否定词”结构进行了遍历研究, 将双重否定结构按照格式分为了3大类, 25小类, 常用双重否定结构或构式132个。结合动词的叙实性、否定焦点、语义否定与语用否定等相关理论, 本文归纳了双重否定结构的三大成立条件, 并据此设计实现了基于规则的双重否定结构自动识别程序。程序实验的精确率为98. 85%, 召回率为98. 90%, F1值为98. 85%。同时, 程序还从96281句语料中获得了8640句精确率约为99%的含有双重否定结构的句子, 为后续基于统计的深度学习模型提供了语料支持的可能。”
no code implementations • ICLR 2019 • Yu Wang, Jack W. Stokes, Mady Marinescu
Antimalware products are a key component in detecting malware attacks, and their engines typically execute unknown programs in a sandbox prior to running them on the native operating system.
no code implementations • ICLR 2019 • Yu Wang, Fengjuan Gao, Amin Alipour, Linzhang Wang, Xuandong Li, Zhendong Su
Boolean satisfiability (SAT) is one of the most well-known NP-complete problems and has been extensively studied.
no code implementations • EMNLP 2020 • Yu Wang, Yun Li, Hanghang Tong, Ziye Zhu
Specifically, we design (1) Head-Tail Detector based on the multi-head self-attention mechanism and bi-affine classifier to detect boundary tokens, and (2) Token Interaction Tagger based on traditional sequence labeling approaches to characterize the internal token connection within the boundary.
no code implementations • CCL 2020 • Yu Wang
“不v1不v2”是汉语中典型的双重否定结构形式之一, 它包括“不+助动词+不+v2”(不得不去)、“不+是+不v2”(不是不好)、述宾结构“不v1... 不v2”(不认为他不去)等多种双重否定结构, 情况复杂。本文以“不v1不v2”为例, 结合“元语否定”、“动词叙实性”、“否定焦点”等概念, 对“不v1不v2”进行了全面的考察, 制定了“不v1不v2”双重否定结构的识别策略。根据识别策略, 设计了双重否定自动识别程序, 并在此过程中补充了助动词表、非叙实动词表等词库。最终, 对28033句语料进行了识别, 识别正确率为97. 87%, 召回率约为93. 10%。
no code implementations • NAACL 2022 • Yu Wang, V.srinivasan@samsung.com V.srinivasan@samsung.com, Hongxia Jin
Knowledge based question answering (KBQA) is a complex task for natural language understanding.
no code implementations • 22 Sep 2023 • Botian Xu, Feng Gao, Chao Yu, Ruize Zhang, Yi Wu, Yu Wang
In this work, we introduce OmniDrones, an efficient and flexible platform tailored for reinforcement learning in drone control, built on Nvidia's Omniverse Isaac Sim.
no code implementations • 5 Sep 2023 • Yusheng Liao, Yutong Meng, Hongcheng Liu, Yanfeng Wang, Yu Wang
A medical consultation training set is further constructed to improve the consultation ability of LLMs.
no code implementations • 5 Sep 2023 • Muhao Liu, Chenyang Qi, Shunxing Bao, Quan Liu, Ruining Deng, Yu Wang, Shilin Zhao, Haichun Yang, Yuankai Huo
However, very few, if any, deep learning based approaches have been applied to kidney layer structure segmentation.
1 code implementation • 31 Aug 2023 • Yi Zhang, Yuying Zhao, Zhaoqing Li, Xueqi Cheng, Yu Wang, Olivera Kotevska, Philip S. Yu, Tyler Derr
Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain.
no code implementations • 28 Aug 2023 • Zhisheng Zheng, Ziyang Ma, Yu Wang, Xie Chen
In recent years, speech-based self-supervised learning (SSL) has made significant progress in various tasks, including automatic speech recognition (ASR).
no code implementations • 27 Aug 2023 • Yu Wang, Xin Xin, Zaiqiao Meng, Xiangnan He, Joemon Jose, Fuli Feng
We employ the proposed DeCA on both the binary label scenario and the multiple label scenario.
no code implementations • 24 Aug 2023 • Karthik Somayaji NS, Yu Wang, Malachi Schram, Jan Drgona, Mahantesh Halappanavar, Frank Liu, Peng Li
Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value function distribution.
no code implementations • 22 Aug 2023 • Yu Wang, Nedim Lipka, Ryan A. Rossi, Alexa Siu, Ruiyi Zhang, Tyler Derr
Concurrently, the LM-guided traverser acts as a local navigator that gathers pertinent context to progressively approach the question and guarantee retrieval quality.
1 code implementation • 22 Aug 2023 • Mohamed Elaraby, Mengyin Lu, Jacob Dunn, Xueying Zhang, Yu Wang, Shizhu Liu, Pingchuan Tian, Yuping Wang, Yuxuan Wang
Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP).
1 code implementation • 20 Aug 2023 • Zihan Zhao, Yiyang Jiang, Heyang Liu, Yanfeng Wang, Yu Wang
While Large Language Models (LLMs) have demonstrated commendable performance across a myriad of domains and tasks, existing LLMs still exhibit a palpable deficit in handling multimodal functionalities, especially for the Spoken Question Answering (SQA) task which necessitates precise alignment and deep interaction between speech and text features.
no code implementations • 16 Aug 2023 • Tianyu Fu, Chiyue Wei, Yu Wang, Rex Ying
Subgraph counting is the problem of counting the occurrences of a given query graph in a large target graph.
1 code implementation • 13 Aug 2023 • Yichen Yuan, Yifan Wang, Lijun Wang, Xiaoqi Zhao, Huchuan Lu, Yu Wang, Weibo Su, Lei Zhang
Recent leading zero-shot video object segmentation (ZVOS) works devote to integrating appearance and motion information by elaborately designing feature fusion modules and identically applying them in multiple feature stages.
1 code implementation • 10 Aug 2023 • Jiayuan Chen, Yu Wang, Ruining Deng, Quan Liu, Can Cui, Tianyuan Yao, Yilin Liu, Jianyong Zhong, Agnes B. Fogo, Haichun Yang, Shilin Zhao, Yuankai Huo
Podocytes, specialized epithelial cells that envelop the glomerular capillaries, play a pivotal role in maintaining renal health.
no code implementations • 7 Aug 2023 • An Yan, Yu Wang, Yiwu Zhong, chengyu dong, Zexue He, Yujie Lu, William Wang, Jingbo Shang, Julian McAuley
Recent advances in foundation models present new opportunities for interpretable visual recognition -- one can first query Large Language Models (LLMs) to obtain a set of attributes that describe each class, then apply vision-language models to classify images via these attributes.
no code implementations • 4 Aug 2023 • Xin Mu, Yu Wang, Yehong Zhang, JiaQi Zhang, Hui Wang, Yang Xiang, Yue Yu
Understanding the life cycle of the machine learning (ML) model is an intriguing area of research (e. g., understanding where the model comes from, how it is trained, and how it is used).
no code implementations • 28 Jul 2023 • Xuefei Ning, Zinan Lin, Zixuan Zhou, Huazhong Yang, Yu Wang
This work aims at decreasing the end-to-end generation latency of large language models (LLMs).
no code implementations • 25 Jul 2023 • Jinxiang Liu, Chen Ju, Chaofan Ma, Yanfeng Wang, Yu Wang, Ya zhang
The goal of the audio-visual segmentation (AVS) task is to segment the sounding objects in the video frames using audio cues.
no code implementations • 17 Jul 2023 • Tianchen Zhao, Xuefei Ning, Ke Hong, Zhongyuan Qiu, Pu Lu, Yali Zhao, Linfeng Zhang, Lipu Zhou, Guohao Dai, Huazhong Yang, Yu Wang
One reason for this high resource consumption is the presence of a large number of redundant background points in Lidar point clouds, resulting in spatial redundancy in both 3D voxel and dense BEV map representations.
no code implementations • 17 Jul 2023 • Yan-Jie Zhou, Wei Liu, Yuan Gao, Jing Xu, Le Lu, Yuping Duan, Hao Cheng, Na Jin, Xiaoyong Man, Shuang Zhao, Yu Wang
Skin diseases are among the most prevalent health issues, and accurate computer-aided diagnosis methods are of importance for both dermatologists and patients.
1 code implementation • 10 Jul 2023 • Yu Wang, Emma R. Cobian, Jubilee Lee, Fang Liu, Jonathan D. Hauenstein, Daniele E. Schiavazzi
Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions.
no code implementations • 10 Jul 2023 • Yuying Zhao, Yu Wang, Yunchao Liu, Xueqi Cheng, Charu Aggarwal, Tyler Derr
Additionally, motivated by the concepts of user-level and item-level fairness, we broaden the understanding of diversity to encompass not only the item level but also the user level.
no code implementations • 8 Jul 2023 • April Chen, Ryan A. Rossi, Namyong Park, Puja Trivedi, Yu Wang, Tong Yu, Sungchul Kim, Franck Dernoncourt, Nesreen K. Ahmed
In this article, we examine and categorize fairness techniques for improving the fairness of GNNs.
no code implementations • 20 Jun 2023 • Yu Wang, Tiebiao Zhao, Fan Yi
This technical report presents our 1st place solution for the Waymo Open Sim Agents Challenge (WOSAC) 2023.
no code implementations • 20 Jun 2023 • Yu Wang, Xuelin Qian, Jingyang Huo, Tiejun Huang, Bo Zhao, Yanwei Fu
Through the adaptation of the Auto-Regressive model and the utilization of large language models, we have developed a remarkable model with an astounding 3. 6 billion trainable parameters, establishing it as the largest 3D shape generation model to date, named Argus-3D.
no code implementations • 15 Jun 2023 • Yu Wang, Tongya Zheng, Shunyu Liu, KaiXuan Chen, Zunlei Feng, Yunzhi Hao, Mingli Song
The human mobility simulation task aims to generate human mobility trajectories given a small set of trajectory data, which have aroused much concern due to the scarcity and sparsity of human mobility data.
no code implementations • 15 Jun 2023 • Ziyang Ma, Zhisheng Zheng, Guanrou Yang, Yu Wang, Chao Zhang, Xie Chen
Our models outperform other SSL models significantly on the LibriSpeech benchmark without the need for iterative re-clustering and re-training.
1 code implementation • 15 Jun 2023 • Enshu Liu, Xuefei Ning, Zinan Lin, Huazhong Yang, Yu Wang
Diffusion probabilistic models (DPMs) are a new class of generative models that have achieved state-of-the-art generation quality in various domains.
no code implementations • CVPR 2023 • Mengxi Chen, Linyu Xing, Yu Wang, Ya zhang
This paper explores the tasks of leveraging auxiliary modalities which are only available at training to enhance multimodal representation learning through cross-modal Knowledge Distillation (KD).
no code implementations • 13 Jun 2023 • Yu Wang, Jingjie Zhang, Junru Jin, Leyi Wei
Molecular representation learning (MRL) is a fundamental task for drug discovery.
no code implementations • 9 Jun 2023 • Pablo Soldati, Euhanna Ghadimi, Burak Demirel, Yu Wang, Raimundas Gaigalas, Mathias Sintorn
Artificial intelligence (AI) has emerged as a powerful tool for addressing complex and dynamic tasks in communication systems, where traditional rule-based algorithms often struggle.
no code implementations • 8 Jun 2023 • Shuo Ye, Shujian Yu, Wenjin Hou, Yu Wang, Xinge You
Fine-grained visual categorization (FGVC) is a challenging task due to similar visual appearances between various species.
no code implementations • 5 Jun 2023 • Shuyang Jiang, Yuhao Wang, Yu Wang
However, while various methods have been proposed to augment LLMs with retrieved knowledge and enhance the quality of code generation, the performance of these retrieval-based methods is limited by the strength of the retrievers used.
no code implementations • 4 Jun 2023 • Shuo Ye, Yufeng Shi, Ruxin Wang, Yu Wang, Jiamiao Xu, Chuanwu Yang, Xinge You
Data is the foundation for the development of computer vision, and the establishment of datasets plays an important role in advancing the techniques of fine-grained visual categorization~(FGVC).
1 code implementation • CVPR 2023 • Yu Wang, Pengchong Qiao, Chang Liu, Guoli Song, Xiawu Zheng, Jie Chen
We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field.
no code implementations • 18 May 2023 • Jinxiang Liu, Yu Wang, Chen Ju, Chaofan Ma, Ya zhang, Weidi Xie
The objective of Audio-Visual Segmentation (AVS) is to localise the sounding objects within visual scenes by accurately predicting pixel-wise segmentation masks.
no code implementations • 11 May 2023 • Kun Su, Judith Yue Li, Qingqing Huang, Dima Kuzmin, Joonseok Lee, Chris Donahue, Fei Sha, Aren Jansen, Yu Wang, Mauro Verzetti, Timo I. Denk
Generating high quality music that complements the visual content of a video is a challenging task.
no code implementations • 4 May 2023 • Yuxuan Yin, Yu Wang, Peng Li
Bayesian optimization (BO) is a powerful tool for seeking the global optimum of black-box functions.
no code implementations • 27 Apr 2023 • Qingpeng Zhu, Wenxiu Sun, Yuekun Dai, Chongyi Li, Shangchen Zhou, Ruicheng Feng, Qianhui Sun, Chen Change Loy, Jinwei Gu, Yi Yu, Yangke Huang, Kang Zhang, Meiya Chen, Yu Wang, Yongchao Li, Hao Jiang, Amrit Kumar Muduli, Vikash Kumar, Kunal Swami, Pankaj Kumar Bajpai, Yunchao Ma, Jiajun Xiao, Zhi Ling
To evaluate the performance of different depth completion methods, we organized an RGB+sparse ToF depth completion competition.
no code implementations • 22 Apr 2023 • Yu Wang, Zhiwei Liu, Liangwei Yang, Philip S. Yu
Generative models have attracted significant interest due to their ability to handle uncertainty by learning the inherent data distributions.
no code implementations • 10 Apr 2023 • Yu Wang, Shuhui Bu, Lin Chen, Yifei Dong, Kun Li, Xuefeng Cao, Ke Li
First, the point cloud is divided into small patches, and a matching patch set is selected based on global descriptors and spatial distribution, which constitutes the coarse matching process.
1 code implementation • F1000Research 2023 • Xiaopeng Xu, Juexiao Zhou, Chen Zhu, Qing Zhan, Zhongxiao Li, Ruochi Zhang, Yu Wang, Xingyu Liao, Xin Gao
The SGPT-RL method achieved better results than Reinvent on the ACE2 task, where molecular docking was used as the optimization goal.
no code implementations • 30 Mar 2023 • Nankai Lin, Hongbin Zhang, Menglan Shen, Yu Wang, Shengyi Jiang, Aimin Yang
Grammatical error correction (GEC) is a challenging task of natural language processing techniques.
1 code implementation • CVPR 2023 • Tianli Zhang, Mengqi Xue, Jiangtao Zhang, Haofei Zhang, Yu Wang, Lechao Cheng, Jie Song, Mingli Song
Most existing online knowledge distillation(OKD) techniques typically require sophisticated modules to produce diverse knowledge for improving students' generalization ability.
1 code implementation • Pattern Recognition 2023 • Dichao Liu, Longjiao Zhao, Yu Wang, Jien Kato
Specifically, this work views the shallow to deep layers of CNNs as “experts” knowledgeable about different perspectives.
Ranked #1 on
Fine-Grained Image Classification
on Stanford Cars
(using extra training data)
no code implementations • 21 Mar 2023 • Zixiang Zhou, Dongqiangzi Ye, Weijia Chen, Yufei Xie, Yu Wang, Panqu Wang, Hassan Foroosh
The proposed LiDARFormer utilizes cross-space global contextual feature information and exploits cross-task synergy to boost the performance of LiDAR perception tasks across multiple large-scale datasets and benchmarks.
no code implementations • 17 Mar 2023 • Chaofan Ma, Yuhuan Yang, Chen Ju, Fei Zhang, Jinxiang Liu, Yu Wang, Ya zhang, Yanfeng Wang
However, the challenges exist as there is one structural difference between generative and discriminative models, which limits the direct use.
no code implementations • 11 Mar 2023 • Yu Wang, Lei Cao, Yizhou Yan, Samuel Madden
Moreover, to effectively handle high dimensional, highly complex data sets which are hard to summarize with simple rules, we propose a localized STAIR approach, called L-STAIR.
no code implementations • 4 Mar 2023 • Yu Wang, Ke Wang
Based on these findings, we present two example uses of the formal definition of patterns: a new method for evaluating the robustness and a new technique for improving the accuracy of code summarization models.
1 code implementation • 24 Feb 2023 • Yu Wang, Mikołaj Kasprzak, Jonathan H. Huggins
Variational Inference (VI) is an attractive alternative to Markov Chain Monte Carlo (MCMC) due to its computational efficiency in the case of large datasets and/or complex models with high-dimensional parameters.
no code implementations • 20 Feb 2023 • Zihan Zhao, Yu Wang, Yanfeng Wang
Multimodal emotion recognition is a challenging research area that aims to fuse different modalities to predict human emotion.
1 code implementation • 11 Feb 2023 • Bingyue Su, Yu Wang, Daniele E. Schiavazzi, Fang Liu
We use normalizing flows (NF), a family of deep generative models, to estimate the probability density of a dataset with differential privacy (DP) guarantees, from which privacy-preserving synthetic data are generated.
1 code implementation • 9 Feb 2023 • Dichao Liu, Toshihiko Yamasaki, Yu Wang, Kenji Mase, Jien Kato
Experimental results on the Statefarm Distracted Driver Detection Dataset and AUC Distracted Driver Dataset show that the proposed approach is highly effective for recognizing distracted driving behaviors from photos: (1) the teacher network's accuracy surpasses the previous best accuracy; (2) the student network achieves very high accuracy with only 0. 42M parameters (around 55% of the previous most lightweight model).
no code implementations • 8 Feb 2023 • Xinyi Yang, Shiyu Huang, Yiwen Sun, Yuxiang Yang, Chao Yu, Wei-Wei Tu, Huazhong Yang, Yu Wang
Goal-conditioned hierarchical reinforcement learning (HRL) provides a promising direction to tackle this challenge by introducing a hierarchical structure to decompose the search space, where the low-level policy predicts primitive actions in the guidance of the goals derived from the high-level policy.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
+2
no code implementations • 3 Feb 2023 • Zihu Wang, Yu Wang, Hanbin Hu, Peng Li
Contrastive learning demonstrates great promise for representation learning.
1 code implementation • 3 Feb 2023 • Chao Yu, Jiaxuan Gao, Weilin Liu, Botian Xu, Hao Tang, Jiaqi Yang, Yu Wang, Yi Wu
A crucial limitation of this framework is that every policy in the pool is optimized w. r. t.
1 code implementation • 2 Feb 2023 • Junbo Zhao, Xuefei Ning, Enshu Liu, Binxin Ru, Zixuan Zhou, Tianchen Zhao, Chen Chen, Jiajin Zhang, Qingmin Liao, Yu Wang
In the first step, we train different sub-predictors on different types of available low-fidelity information to extract beneficial knowledge as low-fidelity experts.
no code implementations • 15 Jan 2023 • Yu Wang
Fourth, it proposes a modular and interpretable framework for unsupervised and weakly-supervised probabilistic topic modeling of time-varying data that combines generative statistical models with computational geometric methods.
1 code implementation • 9 Jan 2023 • Chao Yu, Xinyi Yang, Jiaxuan Gao, Jiayu Chen, Yunfei Li, Jijia Liu, Yunfei Xiang, Ruixin Huang, Huazhong Yang, Yi Wu, Yu Wang
Simply waiting for every robot being ready for the next action can be particularly time-inefficient.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
1 code implementation • CVPR 2023 • Baowei Jiang, Bing Bai, Haozhe Lin, Yu Wang, Yuchen Guo, Lu Fang
Facial information is particularly sensitive in this regard.
no code implementations • CVPR 2023 • Yu Wang, Yadong Li, Hongbin Wang
In this paper, we hypothesize that snippets with similar representations should be considered as the same action class despite the absence of supervision signals on each snippet.
Multiple Instance Learning
Weakly-supervised Temporal Action Localization
+1
1 code implementation • 24 Dec 2022 • ZiCheng Zhang, Yingjie Zhou, Wei Sun, Wei Lu, Xiongkuo Min, Yu Wang, Guangtao Zhai
In recent years, large amounts of effort have been put into pushing forward the real-world application of dynamic digital human (DDH).
no code implementations • 23 Dec 2022 • Aritra Bhowmik, Martin R. Oswald, Yu Wang, Nora Baka, Cees G. M. Snoek
The key idea of our paper is to model object relations as a function of initial class predictions and co-occurrence priors to generate a graph representation of an image for improved classification and bounding box regression.
no code implementations • 20 Dec 2022 • Yu Wang, Hongxia Jin
A coreference resolution system is to cluster all mentions that refer to the same entity in a given context.
no code implementations • 18 Dec 2022 • Yu Wang, Hongxia Jin
In this paper, we introduce a robust semantic frame parsing pipeline that can handle both \emph{OOD} patterns and \emph{OOV} tokens in conjunction with a new complex Twitter dataset that contains long tweets with more \emph{OOD} patterns and \emph{OOV} tokens.
no code implementations • 18 Dec 2022 • Yu Wang, Hongxia Jin
The target of a coreference resolution system is to cluster all mentions that refer to the same entity in a given context.
no code implementations • 8 Dec 2022 • Hengrui Zhang, Qitian Wu, Yu Wang, Shaofeng Zhang, Junchi Yan, Philip S. Yu
Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data.
1 code implementation • 7 Dec 2022 • Yuying Zhao, Yu Wang, Tyler Derr
Although research efforts have been devoted to measuring and mitigating bias, they mainly study bias from the result-oriented perspective while neglecting the bias encoded in the decision-making procedure.
no code implementations • 3 Dec 2022 • Chao Pang, Yu Wang, Yi Jiang, Ruheng Wang, Ran Su, Leyi Wei
Moreover, case study results on targeted molecule generation for the SARS-CoV-2 main protease (Mpro) show that by integrating molecule docking into our model as chemical priori, we successfully generate new small molecules with desired drug-like properties for the Mpro, potentially accelerating the de novo design of Covid-19 drugs.
no code implementations • 1 Dec 2022 • Cheng Wang, Xue Fu, Yu Wang, Guan Gui, Haris Gacanin, Hikmet Sari, Fumiyuki Adachi
Specific emitter identification (SEI) is a potential physical layer authentication technology, which is one of the most critical complements of upper layer authentication.
no code implementations • 28 Nov 2022 • Yu Wang, Jin-Zhu Yu, Hiba Baroud
We propose a scalable nonparametric Bayesian approach to reconstruct the topology of interdependent infrastructure networks from observations of cascading failures.
1 code implementation • 15 Nov 2022 • Yu Wang, Xin Li, Shengzhao Wen, Fukui Yang, Wanping Zhang, Gang Zhang, Haocheng Feng, Junyu Han, Errui Ding
In this paper, we focus on the compression of DETR with knowledge distillation.
1 code implementation • 15 Nov 2022 • Zhaofan Qiu, Yehao Li, Yu Wang, Yingwei Pan, Ting Yao, Tao Mei
In this paper, we propose a novel deep architecture tailored for 3D point cloud applications, named as SPE-Net.
no code implementations • 17 Oct 2022 • Ranran Huang, Yu Wang, Huazhong Yang
Learning discriminative representations for subtle localized details plays a significant role in Fine-grained Visual Categorization (FGVC).
no code implementations • CVPR 2023 • Pengchong Qiao, Zhidan Wei, Yu Wang, Zhennan Wang, Guoli Song, Fan Xu, Xiangyang Ji, Chang Liu, Jie Chen
Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations.
no code implementations • 14 Oct 2022 • Zexue He, Yu Wang, Julian McAuley, Bodhisattwa Prasad Majumder
However, when sensitive information is semantically entangled with the task information of the input, e. g., gender information is predictive for a profession, a fair trade-off between task performance and bias mitigation is difficult to achieve.
1 code implementation • 6 Oct 2022 • Yu Wang, Chao Pang, Yuzhe Wang, Yi Jiang, Junru Jin, Sirui Liang, Quan Zou, Leyi Wei
Leveraging artificial intelligence for automatic retrosynthesis speeds up organic pathway planning in digital laboratories.
1 code implementation • 26 Sep 2022 • Jingyang Lin, Yu Wang, Qi Cai, Yingwei Pan, Ting Yao, Hongyang Chao, Tao Mei
Existing works attempt to solve the problem by explicitly imposing uncertainty on classifiers when OOD inputs are exposed to the classifier during training.
no code implementations • 19 Sep 2022 • Dongqiangzi Ye, Zixiang Zhou, Weijia Chen, Yufei Xie, Yu Wang, Panqu Wang, Hassan Foroosh
LidarMultiNet is extensively tested on both Waymo Open Dataset and nuScenes dataset, demonstrating for the first time that major LiDAR perception tasks can be unified in a single strong network that is trained end-to-end and achieves state-of-the-art performance.
no code implementations • 18 Sep 2022 • Chi Zhang, Yu Wang, Linzhang Wang
The recent breakthroughs in deep learning methods have sparked a wave of interest in learning-based bug detectors.
no code implementations • 13 Sep 2022 • Yinan Yang, Yu Wang, Ying Ji, Heng Qi, Jien Kato
Recently, there is a growing belief that data is unnecessary in OPaI.
1 code implementation • 12 Sep 2022 • Zixiang Zhou, Xiangchen Zhao, Yu Wang, Panqu Wang, Hassan Foroosh
It then uses the feature of the center candidate as the query embedding in the transformer.
Ranked #2 on
3D Object Detection
on waymo cyclist
no code implementations • 29 Aug 2022 • Pengfei Zhu, Xinjie Yao, Yu Wang, Meng Cao, Binyuan Hui, Shuai Zhao, QinGhua Hu
Multi-view learning has progressed rapidly in recent years.
1 code implementation • 27 Aug 2022 • Yu Wang, Hengrui Zhang, Zhiwei Liu, Liangwei Yang, Philip S. Yu
Then we propose Contrastive Variational AutoEncoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation.
1 code implementation • 9 Aug 2022 • Lantu Guo, Yu Wang, Yun Lin, Haitao Zhao, Guan Gui
Automatic modulation classification (AMC) is a key technique for designing non-cooperative communication systems, and deep learning (DL) is applied effectively to AMC for improving classification accuracy.
no code implementations • 7 Aug 2022 • Yifan Hu, Yu Wang
However, due to the inconsistent frequency of human activities, the amount of data for each activity in the human activity dataset is imbalanced.
1 code implementation • 16 Jul 2022 • Zixuan Zhou, Xuefei Ning, Yi Cai, Jiashu Han, Yiping Deng, Yuhan Dong, Huazhong Yang, Yu Wang
Specifically, we train the supernet with a large sharing extent (an easier curriculum) at the beginning and gradually decrease the sharing extent of the supernet (a harder curriculum).
1 code implementation • 14 Jul 2022 • Yu Wang, Guan Gui, Yun Lin, Hsiao-Chun Wu, Chau Yuen, Fumiyuki Adachi
Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel FS-SEI method is proposed, based on deep metric ensemble learning (DMEL).
no code implementations • 11 Jul 2022 • Zihan Zhao, Yanfeng Wang, Yu Wang
The research and applications of multimodal emotion recognition have become increasingly popular recently.
1 code implementation • 11 Jul 2022 • Ting Yao, Yehao Li, Yingwei Pan, Yu Wang, Xiao-Ping Zhang, Tao Mei
Dual-ViT is henceforth able to reduce the computational complexity without compromising much accuracy.
no code implementations • 5 Jul 2022 • Hongzhi Huang, Yu Wang, QinGhua Hu, Ming-Ming Cheng
In this study, we propose a novel method, called Class-Specific Semantic Reconstruction (CSSR), that integrates the power of AE and prototype learning.
1 code implementation • 3 Jul 2022 • Yu Wang, Yuying Zhao, Yi Zhang, Tyler Derr
Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures collaborative effect.
1 code implementation • 30 Jun 2022 • Huitong Chen, Yu Wang, QinGhua Hu
Re-balancing methods are used to alleviate the influence of data imbalance; however, we empirically discover that they would under-fit new classes.
1 code implementation • 24 Jun 2022 • Yushun Dong, Song Wang, Yu Wang, Tyler Derr, Jundong Li
The low transparency on how the structure of the input network influences the bias in GNN outcome largely limits the safe adoption of GNNs in various decision-critical scenarios.
no code implementations • 23 Jun 2022 • Dongqiangzi Ye, Weijia Chen, Zixiang Zhou, Yufei Xie, Yu Wang, Panqu Wang, Hassan Foroosh
This technical report presents the 1st place winning solution for the Waymo Open Dataset 3D semantic segmentation challenge 2022.
1 code implementation • CVPR 2023 • Ying Ji, Yu Wang, Kensaku MORI, Jien Kato
Recent studies have achieved outstanding success in explaining 2D image recognition ConvNets.
1 code implementation • 7 Jun 2022 • Yu Wang, Yuying Zhao, Yushun Dong, Huiyuan Chen, Jundong Li, Tyler Derr
Motivated by our analysis, we propose Fair View Graph Neural Network (FairVGNN) to generate fair views of features by automatically identifying and masking sensitive-correlated features considering correlation variation after feature propagation.
no code implementations • 31 May 2022 • Yu Wang, An Zhang, Xiang Wang, Yancheng Yuan, Xiangnan He, Tat-Seng Chua
This paper proposes Differentiable Invariant Causal Discovery (DICD), utilizing the multi-environment information based on a differentiable framework to avoid learning spurious edges and wrong causal directions.
no code implementations • 23 May 2022 • Yu Wang, Fang Liu
The current work on reinforcement learning (RL) from demonstrations often assumes the demonstrations are samples from an optimal policy, an unrealistic assumption in practice.
no code implementations • 17 May 2022 • Yu Wang, Binbin Zhu, Lingsi Kong, Jianlin Wang, Bin Gao, Jianhua Wang, Dingcheng Tian, YuDong Yao
With the help of deep learning methods, the automatic identification or segmentation of nerves can be realized, assisting doctors in completing nerve block anesthesia accurately and efficiently.
no code implementations • 14 May 2022 • Wenhao Huang, Haifan Gong, huan zhang, Yu Wang, Haofeng Li, Guanbin Li, Hong Shen
CT-based bronchial tree analysis plays an important role in the computer-aided diagnosis for respiratory diseases, as it could provide structured information for clinicians.
no code implementations • 13 May 2022 • Chaoqin Huang, Qinwei Xu, Yanfeng Wang, Yu Wang, Ya zhang
To extend the reconstruction-based anomaly detection architecture to the localized anomalies, we propose a self-supervised learning approach through random masking and then restoring, named Self-Supervised Masking (SSM) for unsupervised anomaly detection and localization.
1 code implementation • 26 Apr 2022 • Yu Wang, Yu Dong, Xuesong Lu, Aoying Zhou
Current deep learning models for code summarization generally follow the principle in neural machine translation and adopt the encoder-decoder framework, where the encoder learns the semantic representations from source code and the decoder transforms the learnt representations into human-readable text that describes the functionality of code snippets.
no code implementations • 21 Apr 2022 • Yu Wang, Shuo Ye, Shujian Yu, Xinge You
In this paper, we present a novel approach for FGVC, which can simultaneously make use of partial yet sufficient discriminative information in environmental cues and also compress the redundant information in class-token with respect to the target.
1 code implementation • 7 Apr 2022 • Zeyu Sun, Monica G. Bobra, Xiantong Wang, Yu Wang, Hu Sun, Tamas Gombosi, Yang Chen, Alfred Hero
We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M- or X-class flare in the future 24 hours, from quiet active regions that do not produce any flare within $\pm 24$ hours.
no code implementations • 6 Apr 2022 • Wenhan Cao, Jingliang Duan, Shengbo Eben Li, Chen Chen, Chang Liu, Yu Wang
Both the primal and dual estimators are learned from data using supervised learning techniques, and the explicit sample size is provided, which enables us to guarantee the quality of each learned estimator in terms of feasibility and optimality.
1 code implementation • 19 Mar 2022 • Junwen Pan, Pengfei Zhu, Kaihua Zhang, Bing Cao, Yu Wang, Dingwen Zhang, Junwei Han, QinGhua Hu
Semantic segmentation with limited annotations, such as weakly supervised semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS), is a challenging task that has attracted much attention recently.
Ranked #24 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
no code implementations • CVPR 2022 • Tianchen Zhao, Niansong Zhang, Xuefei Ning, He Wang, Li Yi, Yu Wang
We propose CodedVTR (Codebook-based Voxel TRansformer), which improves data efficiency and generalization ability for 3D sparse voxel transformers.
1 code implementation • 17 Mar 2022 • Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Xun Chen, Lichao Sun
Furthermore, we propose a Federated learning paradigm with privacy-preserving Relation embedding aggregation (FedR) to tackle the privacy issue in FedE.
no code implementations • 13 Feb 2022 • Yu Wang, Yarong Ji, Hongbing Xiao
Then the tensor was mapped to a matrix which was used to mix the one-hot encoded labels of the above image patches.
1 code implementation • 10 Feb 2022 • Benedek Rozemberczki, Charles Tapley Hoyt, Anna Gogleva, Piotr Grabowski, Klas Karis, Andrej Lamov, Andriy Nikolov, Sebastian Nilsson, Michael Ughetto, Yu Wang, Tyler Derr, Benjamin M Gyori
In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task.
no code implementations • 9 Feb 2022 • Renquan Zhang, Yu Wang, Zheng Lv, Sen Pei
We generate counterfactual simulations to estimate effectiveness of quarantine measures.
1 code implementation • 7 Feb 2022 • Liangwei Yang, Zhiwei Liu, Yu Wang, Chen Wang, Ziwei Fan, Philip S. Yu
We conduct a comprehensive analysis of users' online game behaviors, which motivates the necessity of handling those three characteristics in the online game recommendation.
2 code implementations • 2 Jan 2022 • Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, Chelsea Finn
Machine learning algorithms typically assume that training and test examples are drawn from the same distribution.
1 code implementation • CVPR 2022 • Baisong Guo, Xiaoyun Zhang, HaoNing Wu, Yu Wang, Ya zhang, Yan-Feng Wang
Previous super-resolution (SR) approaches often formulate SR as a regression problem and pixel wise restoration, which leads to a blurry and unreal SR output.
1 code implementation • NeurIPS 2021 • Yu Wang, Jingyang Lin, Jingjing Zou, Yingwei Pan, Ting Yao, Tao Mei
Our work reveals a structured shortcoming of the existing mainstream self-supervised learning methods.
no code implementations • ICCV 2021 • Yang Chen, Yu Wang, Yingwei Pan, Ting Yao, Xinmei Tian, Tao Mei
Correspondingly, we also propose a novel "jury" mechanism, which is particularly effective in learning useful semantic feature commonalities among domains.
Ranked #31 on
Domain Generalization
on PACS
no code implementations • 14 Dec 2021 • Yang Chen, Yingwei Pan, Yu Wang, Ting Yao, Xinmei Tian, Tao Mei
From this point, we present a particular paradigm of self-supervised learning tailored for domain adaptation, i. e., Transferrable Contrastive Learning (TCL), which links the SSL and the desired cross-domain transferability congruently.
no code implementations • 12 Dec 2021 • Weilin Liu, Ye Mu, Chao Yu, Xuefei Ning, Zhong Cao, Yi Wu, Shuang Liang, Huazhong Yang, Yu Wang
These scenarios indeed correspond to the vulnerabilities of the under-test driving policies, thus are meaningful for their further improvements.
1 code implementation • 8 Dec 2021 • Yu Wang, Alfred Hero
In this work, we study the emergence of sparsity and multiway structures in second-order statistical characterizations of dynamical processes governed by partial differential equations (PDEs).
2 code implementations • 1 Dec 2021 • Yu Wang, Yuying Zhao, Neil Shah, Tyler Derr
To this end, we introduce a novel framework, Graph-of-Graph Neural Networks (G$^2$GNN), which alleviates the graph imbalance issue by deriving extra supervision globally from neighboring graphs and locally from stochastic augmentations of graphs.
no code implementations • 23 Nov 2021 • Pengfei Zhu, Hongtao Yu, Kaihua Zhang, Yu Wang, Shuai Zhao, Lei Wang, Tianzhu Zhang, QinGhua Hu
To address this issue, segmentation-based trackers have been proposed that employ per-pixel matching to improve the tracking performance of deformable objects effectively.
1 code implementation • 23 Nov 2021 • Tongya Zheng, Zunlei Feng, Yu Wang, Chengchao Shen, Mingli Song, Xingen Wang, Xinyu Wang, Chun Chen, Hao Xu
Our proposed Dynamic Preference Structure (DPS) framework consists of two stages: structure sampling and graph fusion.
no code implementations • 14 Nov 2021 • Yuzi Yan, Xiaoxiang Li, Xinyou Qiu, Jiantao Qiu, Jian Wang, Yu Wang, Yuan Shen
In this paper, we propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
1 code implementation • NeurIPS 2021 • Jiayu Chen, Yuanxin Zhang, Yuanfan Xu, Huimin Ma, Huazhong Yang, Jiaming Song, Yu Wang, Yi Wu
We motivate our paradigm through a variational perspective, where the learning objective can be decomposed into two terms: task learning on the current task distribution, and curriculum update to a new task distribution.
no code implementations • 28 Oct 2021 • Jiabo He, Wei Liu, Yu Wang, Xingjun Ma, Xian-Sheng Hua
Spinal degeneration plagues many elders, office workers, and even the younger generations.
1 code implementation • NeurIPS 2021 • Huaxiu Yao, Yu Wang, Ying WEI, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, Chelsea Finn
In ATS, for the first time, we design a neural scheduler to decide which meta-training tasks to use next by predicting the probability being sampled for each candidate task, and train the scheduler to optimize the generalization capacity of the meta-model to unseen tasks.
1 code implementation • 22 Oct 2021 • Yu Wang, Charu Aggarwal, Tyler Derr
Recent years have witnessed the significant success of applying graph neural networks (GNNs) in learning effective node representations for classification.
no code implementations • 18 Oct 2021 • Hengrui Zhang, Zhongming Yu, Guohao Dai, Guyue Huang, Yufei Ding, Yuan Xie, Yu Wang
The same data are propagated through the graph structure to perform the same neural operation multiple times in GNNs, leading to redundant computation which accounts for 92. 4% of total operators.
no code implementations • 12 Oct 2021 • Chao Yu, Xinyi Yang, Jiaxuan Gao, Huazhong Yang, Yu Wang, Yi Wu
In this paper, we extend the state-of-the-art single-agent visual navigation method, Active Neural SLAM (ANS), to the multi-agent setting by introducing a novel RL-based planning module, Multi-agent Spatial Planner (MSP). MSP leverages a transformer-based architecture, Spatial-TeamFormer, which effectively captures spatial relations and intra-agent interactions via hierarchical spatial self-attentions.
no code implementations • 29 Sep 2021 • Nianhui Guo, Joseph Bethge, Haojin Yang, Kai Zhong, Xuefei Ning, Christoph Meinel, Yu Wang
Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts, often based on specialized model designs using additional 32-bit components.
no code implementations • 29 Sep 2021 • Yu Wang, Jan Drgona, Jiaxin Zhang, Karthik Somayaji NS, Frank Y Liu, Malachi Schram, Peng Li
Although various flow models based on different transformations have been proposed, there still lacks a quantitative analysis of performance-cost trade-offs between different flows as well as a systematic way of constructing the best flow architecture.
1 code implementation • 22 Sep 2021 • Fan Zhang, Yu Wang, Hua Yang
For the context block, we propose strip pooling module to capture anisotropic and long-range contextual information, which exists in abdominal scene.
1 code implementation • 28 Aug 2021 • Yu Wang, Fang Liu, Daniele E. Schiavazzi
To reduce the computational cost without sacrificing inferential accuracy, we propose Normalizing Flow with Adaptive Surrogate (NoFAS), an optimization strategy that alternatively updates the normalizing flow parameters and surrogate model parameters.
1 code implementation • 26 Aug 2021 • Yu Wang, Zhiwei Liu, Ziwei Fan, Lichao Sun, Philip S. Yu
In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information.
1 code implementation • 25 Aug 2021 • Yu Wang, Tyler Derr
Nevertheless, iterative propagation restricts the information of higher-layer neighborhoods to be transported through and fused with the lower-layer neighborhoods', which unavoidably results in feature smoothing between neighborhoods in different layers and can thus compromise the performance, especially on heterophily networks.
Ranked #23 on
Node Classification
on Cornell
no code implementations • 5 Aug 2021 • Yu Wang, Jingyang Lin, Qi Cai, Yingwei Pan, Ting Yao, Hongyang Chao, Tao Mei
In this paper, we construct a novel probabilistic graphical model that effectively incorporates the low rank promoting prior into the framework of contrastive learning, referred to as LORAC.
no code implementations • ACL 2021 • Jiaqi Guo, Ziliang Si, Yu Wang, Qian Liu, Ming Fan, Jian-Guang Lou, Zijiang Yang, Ting Liu
However, we identify two biases in existing datasets for XDTS: (1) a high proportion of context-independent questions and (2) a high proportion of easy SQL queries.
no code implementations • SEMEVAL 2021 • Jinquan Sun, Qi Zhang, Yu Wang, Lei Zhang
Due to the increasing concerns for data privacy, source-free unsupervised domain adaptation attracts more and more research attention, where only a trained source model is assumed to be available, while the labeled source data remain private.
1 code implementation • 28 Jul 2021 • Sindi Shkodrani, Yu Wang, Marco Manfredi, Nóra Baka
Attempts of learning from hierarchical taxonomies in computer vision have been mostly focusing on image classification.
no code implementations • 27 Jul 2021 • Yu Wang, Yuesong Shen, Daniel Cremers
To learn the direct influence among output nodes in a graph, we propose the Explicit Pairwise Factorized Graph Neural Network (EPFGNN), which models the whole graph as a partially observed Markov Random Field.
no code implementations • 25 Jun 2021 • Yu Wang, Jinchao Li, Tristan Naumann, Chenyan Xiong, Hao Cheng, Robert Tinn, Cliff Wong, Naoto Usuyama, Richard Rogahn, Zhihong Shen, Yang Qin, Eric Horvitz, Paul N. Bennett, Jianfeng Gao, Hoifung Poon
A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hundreds of thousands of papers in a matter of months.
1 code implementation • 13 Jun 2021 • Nianhui Guo, Joseph Bethge, Haojin Yang, Kai Zhong, Xuefei Ning, Christoph Meinel, Yu Wang
Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts.
no code implementations • 6 Jun 2021 • Yu Wang, Yilin Shen, Hongxia Jin
In this paper, we introduce a novel multi-step spoken language understanding system based on adversarial learning that can leverage the multiround user's feedback to update slot values.
no code implementations • 1 Jun 2021 • Yu Wang, Hongxia Jin
In this paper, we present a coarse to fine question answering (CFQA) system based on reinforcement learning which can efficiently processes documents with different lengths by choosing appropriate actions.
1 code implementation • 26 May 2021 • Yu Wang, Alfred Hero
We propose a new graphical model inference procedure, called SG-PALM, for learning conditional dependency structure of high-dimensional tensor-variate data.
no code implementations • 20 May 2021 • Yu Wang, Xin Xin, Zaiqiao Meng, Xiangnan He, Joemon Jose, Fuli Feng
A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference.
no code implementations • 19 May 2021 • Yu Wang, Hejia Luo, Ying Chen, Jun Wang, Rong Li, Bin Wang
Next generation beyond 5G networks are expected to provide both Terabits per second data rate communication services and centimeter-level accuracy localization services in an efficient, seamless and cost-effective manner.
1 code implementation • 8 May 2021 • JiaMing Wang, Zhenfeng Shao, Tao Lu, Xiao Huang, Ruiqian Zhang, Yu Wang
Despite their success, however, low/high spatial resolution pairs are usually difficult to obtain in satellites with a high temporal resolution, making such approaches in SR impractical to use.
no code implementations • CVPR 2021 • Yu Wang, Rui Zhang, Shuo Zhang, Miao Li, Yangyang Xia, Xishan Zhang, Shaoli Liu
The directions of weights, and the gradients, can be divided into domain-specific and domain-invariant parts, and the goal of domain adaptation is to concentrate on the domain-invariant direction while eliminating the disturbance from domain-specific one.
1 code implementation • 2 May 2021 • Zhiwei Liu, Ziwei Fan, Yu Wang, Philip S. Yu
We firstly pre-train a transformer with sequences in a reverse direction to predict prior items.
no code implementations • 16 Apr 2021 • Yu Wang, Lifu Huang, Philip S. Yu, Lichao Sun
Membership inference attacks (MIAs) infer whether a specific data record is used for target model training.
1 code implementation • CVPR 2021 • Tong Wu, Ziwei Liu, Qingqiu Huang, Yu Wang, Dahua Lin
We then perform a systematic study on existing long-tailed recognition methods in conjunction with the adversarial training framework.
1 code implementation • 3 Apr 2021 • Yu Wang, Chee Siang Leow, Akio Kobayashi, Takehito Utsuro, Hiromitsu Nishizaki
This paper describes the ExKaldi-RT online automatic speech recognition (ASR) toolkit that is implemented based on the Kaldi ASR toolkit and Python language.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
no code implementations • 29 Mar 2021 • Kalpa Gunaratna, Yu Wang, Hongxia Jin
Then we learn entity embeddings through this new type of triples.
no code implementations • AAAI Workshop AdvML 2022 • Yi Cai, Xuefei Ning, Huazhong Yang, Yu Wang
It provides high scalability because the paths within an EIO network exponentially increase with the network depth.
no code implementations • 26 Mar 2021 • Alper Kamil Bozkurt, Yu Wang, Miroslav Pajic
We study the problem of learning safe control policies that are also effective; i. e., maximizing the probability of satisfying a linear temporal logic (LTL) specification of a task, and the discounted reward capturing the (classic) control performance.
no code implementations • CVPR 2022 • Minxue Tang, Xuefei Ning, Yitu Wang, Jingwei Sun, Yu Wang, Hai Li, Yiran Chen
In this work, we propose FedCor -- an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL.
1 code implementation • CVPR 2021 • Jialian Wu, Jiale Cao, Liangchen Song, Yu Wang, Ming Yang, Junsong Yuan
Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking.
Ranked #1 on
Instance Segmentation
on nuScenes
2 code implementations • 13 Mar 2021 • Shaowei Chen, Yu Wang, Jie Liu, Yuelin Wang
Aspect sentiment triplet extraction (ASTE), which aims to identify aspects from review sentences along with their corresponding opinion expressions and sentiments, is an emerging task in fine-grained opinion mining.
Aspect Sentiment Triplet Extraction
Machine Reading Comprehension
+2
no code implementations • 10 Mar 2021 • Amir Khazraei, Spencer Hallyburton, Qitong Gao, Yu Wang, Miroslav Pajic
This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS).