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 • 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 • 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 • 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%。
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。”
1 code implementation • 14 Mar 2024 • Yu Wang, Xiaogeng Liu, Yu Li, Muhao Chen, Chaowei Xiao
However, with the integration of additional modalities, MLLMs are exposed to new vulnerabilities, rendering them prone to structured-based jailbreak attacks, where semantic content (e. g., "harmful text") has been injected into the images to mislead MLLMs.
no code implementations • 13 Mar 2024 • Yusheng Liao, Yutong Meng, Yuhao Wang, Hongcheng Liu, Yanfeng Wang, Yu Wang
Large Language Models (LLMs) have demonstrated remarkable proficiency in human interactions, yet their application within the medical field remains insufficiently explored.
no code implementations • 4 Mar 2024 • Yue Yang, Yuqi Lin, Hong Liu, Wenqi Shao, Runjian Chen, Hailong Shang, Yu Wang, Yu Qiao, Kaipeng Zhang, Ping Luo
We call for increased attention to the potential and risks of implicit prompts in the T2I community and further investigation into the capabilities and impacts of implicit prompts, advocating for a balanced approach that harnesses their benefits while mitigating their risks.
no code implementations • 4 Mar 2024 • Yu Wang
Our tutorial offers a comprehensive introduction to the pretrain-finetune paradigm.
1 code implementation • 4 Mar 2024 • Yu Wang, Tongya Zheng, Yuxuan Liang, Shunyu Liu, Mingli Song
To address these challenges, we have tailored a Cross-city mObiLity trAnsformer (COLA) with a dedicated model-agnostic transfer framework by effectively transferring cross-city knowledge for human trajectory simulation.
no code implementations • 1 Mar 2024 • Heyang Liu, Yu Wang, Yanfeng Wang
End-to-end (E2E) approach is gradually replacing hybrid models for automatic speech recognition (ASR) tasks.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 29 Feb 2024 • Ruining Deng, Quan Liu, Can Cui, Tianyuan Yao, Jialin Yue, Juming Xiong, Lining Yu, Yifei Wu, Mengmeng Yin, Yu Wang, Shilin Zhao, Yucheng Tang, Haichun Yang, Yuankai Huo
Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics, treatment evaluation, and clinical research.
no code implementations • 29 Feb 2024 • Guangyi Liu, Yu Wang, Zeyu Feng, Qiyu Wu, Liping Tang, Yuan Gao, Zhen Li, Shuguang Cui, Julian McAuley, Eric P. Xing, Zichao Yang, Zhiting Hu
The vast applications of deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations -- across various data types, such as discrete text/protein sequences and continuous images.
no code implementations • 29 Feb 2024 • Yu Wang, Mengying Xing
We find that Gemini is highly accurate in performing object detection, which is arguably the most common and fundamental task in image analysis for political scientists.
1 code implementation • 28 Feb 2024 • Shiyao Li, Xuefei Ning, Luning Wang, Tengxuan Liu, Xiangsheng Shi, Shengen Yan, Guohao Dai, Huazhong Yang, Yu Wang
Post-training quantization (PTQ) has emerged as a promising technique to reduce the cost of large language models (LLMs).
no code implementations • 28 Feb 2024 • Yusheng Liao, Yanfeng Wang, Yu Wang
Autoregressive (AR) and Non-autoregressive (NAR) models are two types of generative models for Neural Machine Translation (NMT).
no code implementations • 26 Feb 2024 • Peng Gao, Xiao Liu, Yu Wang, Ru-Yue Yuan
To expedite the search process, a random channel selection strategy is employed prior to assessing operation candidates.
no code implementations • 24 Feb 2024 • Sixiao Zheng, Jingyang Huo, Yu Wang, Yanwei Fu
We propose an Intelligent Director framework, utilizing LENS to generate descriptions for images and video frames and combining ChatGPT to generate coherent captions while recommending appropriate music names.
no code implementations • 22 Feb 2024 • Rex Ying, Tianyu Fu, Andrew Wang, Jiaxuan You, Yu Wang, Jure Leskovec
SPMiner combines graph neural networks, order embedding space, and an efficient search strategy to identify network subgraph patterns that appear most frequently in the target graph.
no code implementations • 21 Feb 2024 • Yuying Zhao, Minghua Xu, Huiyuan Chen, Yuzhong Chen, Yiwei Cai, Rashidul Islam, Yu Wang, Tyler Derr
Recommender systems (RSs) have gained widespread applications across various domains owing to the superior ability to capture users' interests.
no code implementations • 19 Feb 2024 • Xuelin Qian, Yu Wang, Simian Luo, yinda zhang, Ying Tai, Zhenyu Zhang, Chengjie Wang, xiangyang xue, Bo Zhao, Tiejun Huang, Yunsheng Wu, Yanwei Fu
In this paper, we extend auto-regressive models to 3D domains, and seek a stronger ability of 3D shape generation by improving auto-regressive models at capacity and scalability simultaneously.
no code implementations • 19 Feb 2024 • Yuying Zhao, Yu Wang, Yi Zhang, Pamela Wisniewski, Charu Aggarwal, Tyler Derr
While recommender systems have been designed to improve the user experience in dating platforms by providing personalized recommendations, increasing concerns about fairness have encouraged the development of fairness-aware recommender systems from various perspectives (e. g., gender and race).
1 code implementation • 19 Feb 2024 • Yu Wang, Zeyuan Zhang, Julian McAuley, Zexue He
To address this issue, we propose Long Video Chat (LVChat), where Frame-Scalable Encoding (FSE) is introduced to dynamically adjust the number of embeddings in alignment with the duration of the video to ensure long videos are not overly compressed into a few embeddings.
no code implementations • 19 Feb 2024 • Hongcheng Liu, Pingjie Wang, Yu Wang, Yanfeng Wang
Video-grounded dialogue generation (VDG) requires the system to generate a fluent and accurate answer based on multimodal knowledge.
no code implementations • 18 Feb 2024 • YiQiu Guo, Yuchen Yang, Ya zhang, Yu Wang, Yanfeng Wang
Structured data offers a sophisticated mechanism for the organization of information.
no code implementations • 17 Feb 2024 • Yu Wang, Amin Javari, Janani Balaji, Walid Shalaby, Tyler Derr, Xiquan Cui
Then, we adaptively aggregate items' neighbor information considering user intention within the learned session.
no code implementations • 13 Feb 2024 • Yuqing Liu, Yu Wang, Lichao Sun, Philip S. Yu
We utilize user history as in-context user preferences to address the first challenge.
no code implementations • 7 Feb 2024 • Yu Wang, Xiusi Chen, Jingbo Shang, Julian McAuley
Existing Large Language Models (LLMs) usually remain static after deployment, which might make it hard to inject new knowledge into the model.
1 code implementation • 6 Feb 2024 • Tao Yuan, Xuefei Ning, Dong Zhou, Zhijie Yang, Shiyao Li, Minghui Zhuang, Zheyue Tan, Zhuyu Yao, Dahua Lin, Boxun Li, Guohao Dai, Shengen Yan, Yu Wang
In contrast, the average context lengths of mainstream benchmarks are insufficient (5k-21k), and they suffer from potential knowledge leakage and inaccurate metrics, resulting in biased evaluation.
no code implementations • 5 Feb 2024 • Yuqian Fu, Yu Wang, Yixuan Pan, Lian Huai, Xingyu Qiu, Zeyu Shangguan, Tong Liu, Lingjie Kong, Yanwei Fu, Luc van Gool, Xingqun Jiang
To address the first question, we introduce several metrics to quantify domain variances and establish a new CD-FSOD benchmark with diverse domain metric values.
no code implementations • 3 Feb 2024 • Lu Wang, Li Chang, Ruipeng Zhang, Kexun Li, Yu Wang, Wei Chen, Xuanlin Feng, Mingwei Sun, Qi Wang, Charles Damien Lu, Jun Zeng, Hua Jiang
Excessive energy intake increased mortality rapidly in the early period of the acute phase.
1 code implementation • 2 Feb 2024 • Kun-Peng Ning, Shuo Yang, Yu-Yang Liu, Jia-Yu Yao, Zhen-Hui Liu, Yu Wang, Ming Pang, Li Yuan
Existing large language models (LLMs) evaluation methods typically focus on testing the performance on some closed-environment and domain-specific benchmarks with human annotations.
1 code implementation • 18 Jan 2024 • Zhenbang Xiao, Shunyu Liu, Yu Wang, Tongya Zheng, Mingli Song
Graph condensation has emerged as an intriguing technique to provide Graph Neural Networks for large-scale graphs with a more compact yet informative small graph to save the expensive costs of large-scale graph learning.
no code implementations • 15 Jan 2024 • Yuhao Wang, Yusheng Liao, Heyang Liu, Hongcheng Liu, Yu Wang, Yanfeng Wang
We believe that these hallucinations are partially due to the models' struggle with understanding what they can and cannot perceive from images, a capability we refer to as self-awareness in perception.
1 code implementation • 15 Jan 2024 • Zhilin Huang, Ling Yang, Zaixi Zhang, Xiangxin Zhou, Yu Bao, Xiawu Zheng, Yuwei Yang, Yu Wang, Wenming Yang
Then the selected protein-ligand subcomplex is processed with SE(3)-equivariant neural networks, and transmitted back to each atom of the complex for augmenting the target-aware 3D molecule diffusion generation with binding interaction information.
no code implementations • 13 Jan 2024 • Yu Hong, Qian Liu, Huayuan Cheng, Danjiao Ma, Hang Dai, Yu Wang, Guangzhi Cao, Yong Ding
The past few years have witnessed the rapid development of vision-centric 3D perception in autonomous driving.
1 code implementation • 12 Jan 2024 • Yu Wang, Junxian Mu, Pengfei Zhu, QinGhua Hu
We show that the differences in attention maps can lead to diverse representations so that the fused representations can well handle the open space.
no code implementations • 12 Jan 2024 • Ji Liu, Dehua Tang, Yuanxian Huang, Li Zhang, Xiaocheng Zeng, Dong Li, Mingjie Lu, Jinzhang Peng, Yu Wang, Fan Jiang, Lu Tian, Ashish Sirasao
Our method also achieves state-of-the-art pruning performance on the vision transformer model.
1 code implementation • 12 Jan 2024 • Pengfei Zhu, Qian Wang, Yu Wang, Jialu Li, QinGhua Hu
In this paper, we propose to dynamically learn the weights of SSL tasks for different nodes and fuse the embeddings learned from different SSL tasks to boost performance.
no code implementations • 8 Jan 2024 • Shulin Zeng, Jun Liu, Guohao Dai, Xinhao Yang, Tianyu Fu, Hongyi Wang, Wenheng Ma, Hanbo Sun, Shiyao Li, Zixiao Huang, Yadong Dai, Jintao Li, Zehao Wang, Ruoyu Zhang, Kairui Wen, Xuefei Ning, Yu Wang
However, existing GPU and transformer-based accelerators cannot efficiently process compressed LLMs, due to the following unresolved challenges: low computational efficiency, underutilized memory bandwidth, and large compilation overheads.
1 code implementation • 27 Dec 2023 • Yan Fan, Yu Wang, Pengfei Zhu, QinGhua Hu
In this work, we focus on semi-supervised continual learning (SSCL), where the model progressively learns from partially labeled data with unknown categories.
1 code implementation • 23 Dec 2023 • Jijia Liu, Chao Yu, Jiaxuan Gao, Yuqing Xie, Qingmin Liao, Yi Wu, Yu Wang
AI agents powered by Large Language Models (LLMs) have made significant advances, enabling them to assist humans in diverse complex tasks and leading to a revolution in human-AI coordination.
no code implementations • 19 Dec 2023 • Xin Mu, Yu Wang, Zhengan Huang, Junzuo Lai, Yehong Zhang, Hui Wang, Yue Yu
In the rapidly growing digital economy, protecting intellectual property (IP) associated with digital products has become increasingly important.
1 code implementation • 19 Dec 2023 • Weipeng Guan, Peiyu Chen, Huibin Zhao, Yu Wang, Peng Lu
To the best of our knowledge, this is the first non-learning work to realize event-based dense mapping.
no code implementations • 19 Dec 2023 • Jiayu Chen, Guosheng Li, Chao Yu, Xinyi Yang, Botian Xu, Huazhong Yang, Yu Wang
In this work, we combine RL and curriculum learning to introduce a flexible solver for multiagent pursuit problems, named TaskFlex Solver (TFS), which is capable of solving multi-agent pursuit problems with diverse and dynamically changing task conditions in both 2-dimensional and 3-dimensional scenarios.
no code implementations • 18 Dec 2023 • Yu Wang, Zhiwei Liu, JianGuo Zhang, Weiran Yao, Shelby Heinecke, Philip S. Yu
With our principle, we managed to outperform GPT-Turbo-3. 5 on three datasets using 7b models e. g., Vicuna-7b and Openchat-7b on NDCG@10.
1 code implementation • 17 Dec 2023 • Yu Wang, Zexue He, Zhankui He, Hao Xu, Julian McAuley
This fine-tuning allows the model to generate explanations that convey the compatibility relationships between items.
1 code implementation • 12 Dec 2023 • Zelin Xu, Tingsong Xiao, Wenchong He, Yu Wang, Zhe Jiang
The problem is challenging due to the sparse and noisy input labels, spatial uncertainty within the label inference process, and high computational costs associated with a large number of sample locations.
no code implementations • 12 Dec 2023 • Enshu Liu, Xuefei Ning, Huazhong Yang, Yu Wang
In this paper, we propose a unified sampling framework (USF) to study the optional strategies for solver.
no code implementations • 5 Dec 2023 • Xinyi Yang, Xinting Yang, Chao Yu, Jiayu Chen, Huazhong Yang, Yu Wang
Besides, to enhance generalization capabilities in scenarios with unseen team sizes, we divide agents into multiple groups, each with a previously trained number of agents.
no code implementations • 28 Nov 2023 • Jinhao Li, Shiyao Li, Jiaming Xu, Shan Huang, Yaoxiu Lian, Jun Liu, Yu Wang, Guohao Dai
Weights are quantized by groups, while the ranges of weights are large in some groups, resulting in large quantization errors and nonnegligible accuracy loss (e. g. >3% for Llama2-7b with 2-bit quantization in GPTQ and Greenbit).
no code implementations • 15 Nov 2023 • Hendrik Buschmeier, Heike M. Buhl, Friederike Kern, Angela Grimminger, Helen Beierling, Josephine Fisher, André Groß, Ilona Horwath, Nils Klowait, Stefan Lazarov, Michael Lenke, Vivien Lohmer, Katharina Rohlfing, Ingrid Scharlau, Amit Singh, Lutz Terfloth, Anna-Lisa Vollmer, Yu Wang, Annedore Wilmes, Britta Wrede
Explainability has become an important topic in computer science and artificial intelligence, leading to a subfield called Explainable Artificial Intelligence (XAI).
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1
no code implementations • 12 Nov 2023 • Chenyu Wang, Zhen Dong, Daquan Zhou, Zhenhua Zhu, Yu Wang, Jiashi Feng, Kurt Keutzer
On the hardware side, we modify the datapath of current PIM accelerators to accommodate epitomes and implement a feature map reuse technique to reduce computation cost.
1 code implementation • 8 Nov 2023 • Ruochi Zhang, Haoran Wu, Yuting Xiu, Kewei Li, Ningning Chen, Yu Wang, Yan Wang, Xin Gao, Fengfeng Zhou
In recent years, the scientific community has become increasingly interested on peptides with non-canonical amino acids due to their superior stability and resistance to proteolytic degradation.
no code implementations • 2 Nov 2023 • Ke Hong, Guohao Dai, Jiaming Xu, Qiuli Mao, Xiuhong Li, Jun Liu, Kangdi Chen, Yuhan Dong, Yu Wang
A single and static dataflow may lead to a 50. 25% performance loss for GEMMs of different shapes in LLM inference.
no code implementations • 1 Nov 2023 • Xinyi Yang, Yuxiang Yang, Chao Yu, Jiayu Chen, Jingchen Yu, Haibing Ren, Huazhong Yang, Yu Wang
In this paper, we propose Multi-Agent Neural Topological Mapping (MANTM) to improve exploration efficiency and generalization for multi-agent exploration tasks.
1 code implementation • 30 Oct 2023 • Qiao Sun, Shiduo Zhang, Danjiao Ma, Jingzhe Shi, Derun Li, Simian Luo, Yu Wang, Ningyi Xu, Guangzhi Cao, Hang Zhao
STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task.
no code implementations • 29 Oct 2023 • Zelai Xu, Chao Yu, Fei Fang, Yu Wang, Yi Wu
To mitigate the intrinsic bias in language actions, our agents use an LLM to perform deductive reasoning and generate a diverse set of action candidates.
1 code implementation • 25 Oct 2023 • Haotian Tang, Shang Yang, Zhijian Liu, Ke Hong, Zhongming Yu, Xiuyu Li, Guohao Dai, Yu Wang, Song Han
On top of this, we design the Sparse Autotuner, which extends the design space of existing sparse convolution libraries and searches for the best dataflow configurations for training and inference workloads.
no code implementations • 21 Oct 2023 • Zexue He, Yu Wang, An Yan, Yao Liu, Eric Y. Chang, Amilcare Gentili, Julian McAuley, Chun-Nan Hsu
Curated datasets for healthcare are often limited due to the need of human annotations from experts.
no code implementations • 19 Oct 2023 • Yu Wang, Yuxuan Yin, Karthik Somayaji Nanjangud Suryanarayana, Jan Drgona, Malachi Schram, Mahantesh Halappanavar, Frank Liu, Peng Li
Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge.
1 code implementation • 11 Oct 2023 • Osman Ülger, Yu Wang, Ysbrand Galama, Sezer Karaoglu, Theo Gevers, Martin R. Oswald
Humans have a remarkable ability to perceive and reason about the world around them by understanding the relationships between objects.
1 code implementation • 9 Oct 2023 • Haoyu Zhang, Yu Wang, Guanghao Yin, Kejun Liu, Yuanyuan Liu, Tianshu Yu
Though Multimodal Sentiment Analysis (MSA) proves effective by utilizing rich information from multiple sources (e. g., language, video, and audio), the potential sentiment-irrelevant and conflicting information across modalities may hinder the performance from being further improved.
Ranked #1 on Multimodal Sentiment Analysis on CMU-MOSEI (Acc-7 metric)
no code implementations • 8 Oct 2023 • Yu Wang, Yihong Wang, Tong Liu, Xiubao Sui, Qian Chen
In this paper, we propose a novel Retinex-based method, called ITRE, which suppresses noise and artifacts from the origin of the model, prevents over-exposure throughout the enhancement process.
no code implementations • 7 Oct 2023 • Yuchen Yang, Houqiang Li, Yanfeng Wang, Yu Wang
In this study, we introduce an uncertainty-aware in-context learning framework to empower the model to enhance or reject its output in response to uncertainty.
no code implementations • 7 Oct 2023 • Jiayu Chen, Zelai Xu, Yunfei Li, Chao Yu, Jiaming Song, Huazhong Yang, Fei Fang, Yu Wang, Yi Wu
In this work, we present a novel subgame curriculum learning framework for zero-sum games.
1 code implementation • 6 Oct 2023 • Yu Wang, Tong Zhao, Yuying Zhao, Yunchao Liu, Xueqi Cheng, Neil Shah, Tyler Derr
Despite the widespread belief that low-degree nodes exhibit poorer LP performance, our empirical findings provide nuances to this viewpoint and prompt us to propose a better metric, Topological Concentration (TC), based on the intersection of the local subgraph of each node with the ones of its neighbors.
no code implementations • 5 Oct 2023 • Zelai Xu, Yancheng Liang, Chao Yu, Yu Wang, Yi Wu
Alternatively, Policy-Space Response Oracles (PSRO) is an iterative framework for learning NE, where the best responses w. r. t.
no code implementations • 4 Oct 2023 • An Yan, Yu Wang, Yiwu Zhong, Zexue He, Petros Karypis, Zihan Wang, chengyu dong, Amilcare Gentili, Chun-Nan Hsu, Jingbo Shang, Julian McAuley
Medical image classification is a critical problem for healthcare, with the potential to alleviate the workload of doctors and facilitate diagnoses of patients.
no code implementations • 26 Sep 2023 • Hongcheng Liu, Zhe Chen, Hui Li, Pingjie Wang, Yanfeng Wang, Yu Wang
Generating dialogue grounded in videos requires a high level of understanding and reasoning about the visual scenes in the videos.
1 code implementation • 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).
1 code implementation • bioRxiv 2023 • Tingpeng Yang, Tianze Ling, Boyan Sun, Zhendong Liang, Fan Xu, Xiansong Huang, Linhai Xie, Yonghong He, Leyuan Li, Fuchu He, Yu Wang, Cheng Chang
De novo peptide sequencing is a promising approach for novel peptide discovery.
no code implementations • 27 Aug 2023 • Yu Wang, Xin Xin, Zaiqiao Meng, 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.
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 • 22 Aug 2023 • Yu Wang, Nedim Lipka, Ryan A. Rossi, Alexa Siu, Ruiyi Zhang, Tyler Derr
Concurrently, the graph traversal agent acts as a local navigator that gathers pertinent context to progressively approach the question and guarantee retrieval quality.
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.
1 code implementation • 16 Aug 2023 • Tianyu Fu, Chiyue Wei, Yu Wang, Rex Ying
We introduce DeSCo, a scalable neural deep subgraph counting pipeline, designed to accurately predict both the count and occurrence position of queries on target graphs post single training.
1 code implementation • ICCV 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.
1 code implementation • ICCV 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).
1 code implementation • 28 Jul 2023 • Xuefei Ning, Zinan Lin, Zixuan Zhou, Zifu Wang, 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 • ICCV 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, 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 • 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 • 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.
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.
1 code implementation • 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.
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 • 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 • 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 radio communication systems.
1 code implementation • 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
Video-to-music generation demands both a temporally localized high-quality listening experience and globally aligned video-acoustic signatures.
no code implementations • 4 May 2023 • Yuxuan Yin, Yu Wang, Peng Li
$\texttt{TSBO}$ incorporates a teacher model, an unlabeled data sampler, and a student model.
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.
1 code implementation • IEEE Transactions on Circuits and Systems for Video Technology 2023 • Guosong Jiang, Pengfei Zhu, Yu Wang, QinGhua Hu
In this paper, we point out that balancing between structural risk and open space risk is crucial for open set recognition, and re-formalize it as open set structural risk.
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.
2 code implementations • 2 Mar 2023 • Sheng Zhang, Yanbo Xu, Naoto Usuyama, Hanwen Xu, Jaspreet Bagga, Robert Tinn, Sam Preston, Rajesh Rao, Mu Wei, Naveen Valluri, Cliff Wong, Andrea Tupini, Yu Wang, Matt Mazzola, Swadheen Shukla, Lars Liden, Jianfeng Gao, Matthew P. Lungren, Tristan Naumann, Sheng Wang, Hoifung Poon
Therefore, training an effective generalist biomedical model requires high-quality multimodal data, such as parallel image-text pairs.
Ranked #3 on Medical Visual Question Answering on SLAKE-English
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).
1 code implementation • 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.
2 code implementations • 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
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 Action Localization +2
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 • ICCV 2023 • Haozhe Lin, Zequn Chen, Jinzhi Zhang, Bing Bai, Yu Wang, Ruqi Huang, Lu Fang
The CGG task capitalizes on the calibrated multiview videos of a dynamic scene, and targets at recovering semantic information (coordination, trajectories and relationships) of the presented objects in the form of spatio-temporal context graph in 4D space.
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 • ICCV 2023 • Aritra Bhowmik, Yu Wang, Nora Baka, Martin R. Oswald, Cees G. M. Snoek
Contrary to existing methods, which learn objects and relations separately, our key idea is to learn the object-relation distribution jointly.
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 • 19 Dec 2022 • Peng Gao, Feng Gao, Jian-Cheng Ni, Yu Wang, Fei Wang
Drug-drug interaction prediction is a crucial issue in molecular biology.
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.
1 code implementation • 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.
2 code implementations • 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.
1 code implementation • 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.
no code implementations • 2022/08/15 2022 • Yu Wang, Wenbin, FENG Chongchong YU, Xinyu Hu, Yuqiu ZHANG4
In order to solve the problems of low model accuracy, poor computing power, poor parallel ability and excessive power consumption in the deployment of RGBD based 3 D target detection model at the embedded end, this paper first proposes an improved RGBD 3 D target detection model based on ENet semantic segmentation model, which takes ENet as the semantic segmentation network, RGB image and depth information are fused to realize 3 D target detection. Secondly, in order to apply the model at the edge, this paper constructs a lightweight network and cuts the network in the down-sampling stage of ENet model. Finally, this paper uses Xilinx ZCU104 as the hardware development kit, which takes FPGA as the auxiliary parallel operation unit and ARM as the main operation unit. It is a heterogeneous computing architecture with the ability to deal with complex operations. The architecture uses FPGA to accelerate the depth model in parallel, which improves the operation speed and reduces the power consumption. The test results of the model on ZCU104 are compared with other hardware. The results show that while ensuring the accuracy, the power consumption of the heterogeneous computing architecture used in this paper is 93% lowerthan that of Intel Xeon e5-2620 v4 CPU, the speed is 12 times higher, and the speed is more than 180 times higher than that of ARM Cortex-A53 commonly used at the edge.
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).
2 code implementations • 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 #33 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 • 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 • 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 #36 on Domain Generalization on PACS
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.