1 code implementation • 18 May 2024 • Zhijie Zhong, Zhiwen Yu, Xing Xi, Yue Xu, Jiahui Chen, Kaixiang Yang
Despite the prevalence of reconstruction-based deep learning methods, time series anomaly detection remains challenging.
1 code implementation • 25 Mar 2024 • Yue Xu, Wenjie Wang
Prompt-based learning is a new language model training paradigm that adapts the Pre-trained Language Models (PLMs) to downstream tasks, which revitalizes the performance benchmarks across various natural language processing (NLP) tasks.
1 code implementation • 26 Jan 2024 • Hao Chen, Yuanchen Bei, Qijie Shen, Yue Xu, Sheng Zhou, Wenbing Huang, Feiran Huang, Senzhang Wang, Xiao Huang
Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational complexity involved in aggregating billions of neighbors.
1 code implementation • 1 Dec 2023 • Ziyu Wang, Yue Xu, Cewu Lu, Yong-Lu Li
It first distills the videos into still images as static memory and then compensates the dynamic and motion information with a learnable dynamic memory block.
no code implementations • ICCV 2023 • Yue Xu, Yong-Lu Li, Zhemin Huang, Michael Xu Liu, Cewu Lu, Yu-Wing Tai, Chi-Keung Tang
With the surge in attention to Egocentric Hand-Object Interaction (Ego-HOI), large-scale datasets such as Ego4D and EPIC-KITCHENS have been proposed.
1 code implementation • 23 Jul 2023 • Guopeng Li, Yue Xu, Jian Ding, Gui-Song Xia
To this end, we propose a generic white-box attack, LGP (local perturbations with adaptively global attacks), to blind mainstream object detectors with controllable perturbations.
1 code implementation • 28 May 2023 • Yue Xu, Yong-Lu Li, Kaitong Cui, Ziyu Wang, Cewu Lu, Yu-Wing Tai, Chi-Keung Tang
Our method consistently enhances the distillation algorithms, even on much larger-scale and more heterogeneous datasets, e. g. ImageNet-1K and Kinetics-400.
no code implementations • 21 May 2023 • Yue Xu, Qijie Shen, Jianwen Yin, Zengde Deng, Dimin Wang, Hao Chen, Lixiang Lai, Tao Zhuang, Junfeng Ge
Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms.
no code implementations • 21 May 2023 • Yue Xu, Hao Chen, Zefan Wang, Jianwen Yin, Qijie Shen, Dimin Wang, Feiran Huang, Lixiang Lai, Tao Zhuang, Junfeng Ge, Xia Hu
Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications.
no code implementations • 1 Mar 2023 • Long Tang, Dengpan Ye, Zhenhao Lu, Yunming Zhang, Shengshan Hu, Yue Xu, Chuanxi Chen
Adversarial example is a rising way of protecting facial privacy security from deepfake modification.
no code implementations • ICCV 2023 • Yong-Lu Li, Yue Xu, Xinyu Xu, Xiaohan Mao, Yuan YAO, SiQi Liu, Cewu Lu
To support OCL, we build a densely annotated knowledge base including extensive labels for three levels of object concept (category, attribute, affordance), and the causal relations of three levels.
no code implementations • 25 Sep 2022 • Yue Xu, Hao Chen, Zengde Deng, Yuanchen Bei, Feiran Huang
Third, we propose a layer ensemble technique which improves the expressiveness of the learned representations by assembling the layer-wise neighborhood representations at the final layer.
no code implementations • 25 Sep 2022 • Qiwei Chen, Yue Xu, Changhua Pei, Shanshan Lv, Tao Zhuang, Junfeng Ge
The results verify that the proposed model outperforms existing CTR models considerably, in terms of both CTR prediction performance and online cost-efficiency.
no code implementations • 25 Sep 2022 • Hao Chen, Zefan Wang, Yue Xu, Xiao Huang, Feiran Huang
State-of-the-art solutions rely on training hybrid models for both cold-start and existing users/items, based on the auxiliary information.
no code implementations • 1 Sep 2022 • Jianyuan Yu, William W. Howard, Yue Xu, R. Michael Buehrer
Model order estimation (MOE) is often a pre-requisite for Direction of Arrival (DoA) estimation.
1 code implementation • 4 Aug 2022 • Yue Xu, Yong-Lu Li, Jiefeng Li, Cewu Lu
Previous methods tackle with data imbalance from the viewpoints of data distribution, feature space, and model design, etc.
1 code implementation • SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2022 Pages 2565–2571 2022 • Hao Chen, Zefan Wang, Feiran Huang, Xiao Huang, Yue Xu, Yishi Lin, Peng He, Zhoujun Li Authors Info & Claims
Embedding-based recommendation models provide recommendations by learning embeddings for each user and item from historical interactions.
no code implementations • 23 Jun 2022 • Xufeng Qian, Yue Xu, Fuyu Lv, Shengyu Zhang, Ziwen Jiang, Qingwen Liu, Xiaoyi Zeng, Tat-Seng Chua, Fei Wu
RSs typically put a large number of items into one page to reduce excessive resource consumption from numerous paging requests, which, however, would diminish the RSs' ability to timely renew the recommendations according to users' real-time interest and lead to a poor user experience.
no code implementations • 5 May 2022 • Xin Chen, Qingtao Tang, Ke Hu, Yue Xu, Shihang Qiu, Jia Cheng, Jun Lei
In Meituan, one of the largest e-commerce platform in China, an item is typically displayed with its image and whether a user clicks the item or not is usually influenced by its image, which implies that user's image behaviors are helpful for understanding user's visual preference and improving the accuracy of CTR prediction.
no code implementations • 30 Mar 2022 • Hao Chen, Zhong Huang, Yue Xu, Zengde Deng, Feiran Huang, Peng He, Zhoujun Li
The experimental results verify that our proposed NEGCN framework can significantly enhance the performance for various typical GCN models on both node classification and recommendation tasks.
3 code implementations • 14 Feb 2022 • Yong-Lu Li, Xinpeng Liu, Xiaoqian Wu, Yizhuo Li, Zuoyu Qiu, Liang Xu, Yue Xu, Hao-Shu Fang, Cewu Lu
Human activity understanding is of widespread interest in artificial intelligence and spans diverse applications like health care and behavior analysis.
1 code implementation • 9 Oct 2021 • Yong-Lu Li, Yue Xu, Xinyu Xu, Xiaohan Mao, Cewu Lu
To model the compositional nature of these concepts, it is a good choice to learn them as transformations, e. g., coupling and decoupling.
no code implementations • 3 Sep 2021 • Bemali Wickramanayake, Zhipeng He, Chun Ouyang, Catarina Moreira, Yue Xu, Renuka Sindhgatta
In this paper, we address the "black-box" problem in predictive process analytics by building interpretable models that are capable to inform both what and why is a prediction.
no code implementations • 9 May 2021 • Hao Chen, Zengde Deng, Yue Xu, Zhoujun Li
In this way, each node can be directly represented by concatenating the information extracted independently from each hop of its neighbors thereby avoiding the recursive neighborhood expansion across layers.
no code implementations • 18 Mar 2021 • Kai Chen, Qinglei Kong, Yijue Dai, Yue Xu, Feng Yin, Lexi Xu, Shuguang Cui
Empowered by big data and machine learning, next-generation data-driven communication systems will be intelligent with the characteristics of expressiveness, scalability, interpretability, and especially uncertainty modeling, which can confidently involve diversified latent demands and personalized services in the foreseeable future.
1 code implementation • 16 Jan 2021 • Shaopeng Fu, Fengxiang He, Yue Xu, DaCheng Tao
This paper proposes a {\it Bayesian inference forgetting} (BIF) framework to realize the right to be forgotten in Bayesian inference.
no code implementations • 10 Jul 2020 • Jianyuan Yu, Yue Xu, Hussein Metwaly Saad, R. Michael Buehrer
With the increasing power of machine learning-based reasoning, the use of meta-information (e. g., digital signal modulation parameters, channel conditions, etc.)
no code implementations • 7 Jun 2020 • Yue Xu, Hao Chen, Zengde Deng, Junxiong Zhu, Yanghua Li, Peng He, Wenyao Gao, Wenjun Xu
The results verify that the proposed model outperforms existing GCN models considerably and yields up to a few orders of magnitude speedup in training, in terms of the recommendation performance.
2 code implementations • CVPR 2020 • Yong-Lu Li, Liang Xu, Xinpeng Liu, Xijie Huang, Yue Xu, Shiyi Wang, Hao-Shu Fang, Ze Ma, Mingyang Chen, Cewu Lu
In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics.
Ranked #3 on Human-Object Interaction Detection on HICO
1 code implementation • CVPR 2020 • Yong-Lu Li, Yue Xu, Xiaohan Mao, Cewu Lu
To model the compositional nature of these general concepts, it is a good choice to learn them through transformations, such as coupling and decoupling.
Ranked #1 on Compositional Zero-Shot Learning on MIT-States (Top-1 accuracy % metric)
no code implementations • 8 Mar 2020 • Feng Yin, Zhidi Lin, Yue Xu, Qinglei Kong, Deshi Li, Sergios Theodoridis, Shuguang, Cui
In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods.
no code implementations • 1 Mar 2020 • Yue Xu, Feng Yin, Wenjun Xu, Chia-Han Lee, Jia-Ru Lin, Shuguang Cui
The marriage of wireless big data and machine learning techniques revolutionizes the wireless system by the data-driven philosophy.
no code implementations • 10 Jul 2019 • Hao Chen, Yue Xu, Feiran Huang, Zengde Deng, Wenbing Huang, Senzhang Wang, Peng He, Zhoujun Li
In this paper, we consider the problem of node classification and propose the Label-Aware Graph Convolutional Network (LAGCN) framework which can directly identify valuable neighbors to enhance the performance of existing GCN models.
no code implementations • 2 Jul 2019 • Yue Xu, Zengde Deng, Mengdi Wang, Wenjun Xu, Anthony Man-Cho So, Shuguang Cui
The recent success of single-agent reinforcement learning (RL) in Internet of things (IoT) systems motivates the study of multi-agent reinforcement learning (MARL), which is more challenging but more useful in large-scale IoT.
no code implementations • 3 Jun 2019 • Yue Xu, Wenjun Xu, Zhi Wang, Jia-Ru Lin, Shuguang Cui
Third, this work proposes an offline-evaluation based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way.
4 code implementations • 13 Apr 2019 • Yong-Lu Li, Liang Xu, Xinpeng Liu, Xijie Huang, Yue Xu, Mingyang Chen, Ze Ma, Shiyi Wang, Hao-Shu Fang, Cewu Lu
To address these and promote the activity understanding, we build a large-scale Human Activity Knowledge Engine (HAKE) based on the human body part states.
Ranked #2 on Human-Object Interaction Detection on HICO (using extra training data)
no code implementations • 13 Feb 2019 • Yue Xu, Feng Yin, Wenjun Xu, Jia-Ru Lin, Shuguang Cui
First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions.
no code implementations • 6 Jan 2019 • Yang Deng, Yao Sun, Yongpei Zhu, Yue Xu, Qianxi Yang, Shuo Zhang, Mingwang Zhu, Jirang Sun, Weiling Zhao, Xiaobo Zhou, Kehong Yuan
In this paper, we propose a new criterion to evaluate efforts of doctors annotating medical image.
no code implementations • 1 Aug 2018 • Yao Sun, Yang Deng, Yue Xu, Shuo Zhang, Mingwang Zhu, Kehong Yuan
Magnetic Resonance Imaging (MRI) is widely used in the pathological and functional studies of the brain, such as epilepsy, tumor diagnosis, etc.
no code implementations • International Joint Conference on Artificial Intelligence 2018 • Yue Xu, Fei Yin, Zhaoxiang Zhang, Cheng-Lin Liu
Layout analysis is a fundamental process in document image analysis and understanding.