no code implementations • 4 Jan 2025 • Yaodan Xu, Sheng Zhou, Zhisheng Niu
For servers incorporating parallel computing resources, batching is a pivotal technique for providing efficient and economical services at scale.
1 code implementation • 3 Jan 2025 • Weizhi Zhang, Yuanchen Bei, Liangwei Yang, Henry Peng Zou, Peilin Zhou, Aiwei Liu, Yinghui Li, Hao Chen, Jianling Wang, Yu Wang, Feiran Huang, Sheng Zhou, Jiajun Bu, Allen Lin, James Caverlee, Fakhri Karray, Irwin King, Philip S. Yu
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations.
no code implementations • 16 Dec 2024 • Bowen Xie, Sheng Zhou, Zhisheng Niu, Hao Wu, Cong Shi
Future Vehicle-to-Everything (V2X) scenarios require high-speed, low-latency, and ultra-reliable communication services, particularly for applications such as autonomous driving and in-vehicle infotainment.
no code implementations • 13 Dec 2024 • Ming Gu, Zhuonan Zheng, Sheng Zhou, Meihan Liu, Jiawei Chen, Tanyu Qiao, Liangcheng Li, Jiajun Bu
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, such as transaction and social net-works.
1 code implementation • 26 Nov 2024 • Yuanchen Bei, Weizhi Chen, Hao Chen, Sheng Zhou, Carl Yang, Jiapei Fan, Longtao Huang, Jiajun Bu
Multi-label node classification is an important yet under-explored domain in graph mining as many real-world nodes belong to multiple categories rather than just a single one.
1 code implementation • 20 Nov 2024 • Siwei Chen, Yinsong, Wang, Ziyi Song, Sheng Zhou
Achieving high levels of safety and reliability in autonomous driving remains a critical challenge, especially due to occlusion and limited perception ranges in standalone systems.
1 code implementation • 19 Nov 2024 • Qingsong Lv, Jiasheng Sun, Sheng Zhou, Xu Zhang, Liangcheng Li, Yun Gao, Sun Qiao, Jie Song, Jiajun Bu
To reduce computational overhead while maintaining model performance, model pruning techniques have been proposed.
1 code implementation • 31 Oct 2024 • Weiqin Yang, Jiawei Chen, Xin Xin, Sheng Zhou, Binbin Hu, Yan Feng, Chun Chen, Can Wang
To address these issues, this work extends SL to a new family of loss functions, termed Pairwise Softmax Loss (PSL), which replaces the exponential function in SL with other appropriate activation functions.
no code implementations • 2 Oct 2024 • Zhe Wang, Tianjian Zhao, Zhen Zhang, Jiawei Chen, Sheng Zhou, Yan Feng, Chun Chen, Can Wang
Our theoretical results demonstrate that HopeDGN can achieve expressive power equivalent to the 2-DWL test.
no code implementations • 29 Sep 2024 • Ruiqing Mao, Haotian Wu, Yukuan Jia, Zhaojun Nan, Yuxuan Sun, Sheng Zhou, Deniz Gündüz, Zhisheng Niu
Collaborative perception (CP) is emerging as a promising solution to the inherent limitations of stand-alone intelligence.
1 code implementation • 22 Sep 2024 • Sheng Zhou, Junbin Xiao, Xun Yang, Peipei Song, Dan Guo, Angela Yao, Meng Wang, Tat-Seng Chua
In this paper, we propose to study Grounded TextVideoQA by forcing models to answer questions and spatio-temporally localize the relevant scene-text regions, thus decoupling QA from scenetext recognition and promoting research towards interpretable QA.
no code implementations • 15 Sep 2024 • Rui Graca, Sheng Zhou, Brian Mcreynolds, Tobi Delbruck
This paper reports a Dynamic Vision Sensor (DVS) event camera that is 6x more sensitive at 14x lower illumination than existing commercial and prototype cameras.
no code implementations • 22 Aug 2024 • Chunpeng Zhou, Kangjie Ning, Qianqian Shen, Sheng Zhou, Zhi Yu, Haishuai Wang
However, these approaches still predominantly necessitate the utilization of domain specific expert-level prompts during the evaluation phase, which severely constrains the model's practicality.
no code implementations • 15 Aug 2024 • Bohao Wang, Feng Liu, Changwang Zhang, Jiawei Chen, Yudi Wu, Sheng Zhou, Xingyu Lou, Jun Wang, Yan Feng, Chun Chen, Can Wang
However, employing LLMs for denoising in sequential recommendation presents notable challenges: 1) Direct application of pretrained LLMs may not be competent for the denoising task, frequently generating nonsensical responses; 2) Even after fine-tuning, the reliability of LLM outputs remains questionable, especially given the complexity of the denoising task and the inherent hallucinatory issue of LLMs.
1 code implementation • 24 Jul 2024 • Zhe Wang, Sheng Zhou, Jiawei Chen, Zhen Zhang, Binbin Hu, Yan Feng, Chun Chen, Can Wang
To this end, we propose a novel Correlated Spatial-Temporal Positional encoding that incorporates a parameter-free personalized interaction intensity estimation under the weak assumption of the Poisson Point Process.
1 code implementation • 9 Jul 2024 • Meihan Liu, Zhen Zhang, Jiachen Tang, Jiajun Bu, Bingsheng He, Sheng Zhou
Unsupervised Graph Domain Adaptation (UGDA) involves the transfer of knowledge from a label-rich source graph to an unlabeled target graph under domain discrepancies.
1 code implementation • 30 Jun 2024 • Qingyun Sun, Ziying Chen, Beining Yang, Cheng Ji, Xingcheng Fu, Sheng Zhou, Hao Peng, JianXin Li, Philip S. Yu
To fill this gap, we develop a comprehensive Graph Condensation Benchmark (GC-Bench) to analyze the performance of graph condensation in different scenarios systematically.
no code implementations • 25 Jun 2024 • Jintao Yan, Tan Chen, Yuxuan Sun, Zhaojun Nan, Sheng Zhou, Zhisheng Niu
In this paper, we formulate a stochastic optimization problem to optimize the VFL training performance, considering the energy constraints and mobility of vehicles, and propose a V2V-enhanced dynamic scheduling (VEDS) algorithm to solve it.
1 code implementation • 18 Jun 2024 • Changhao Li, Haoling Li, Mengqi Xue, Gongfan Fang, Sheng Zhou, Zunlei Feng, Huiqiong Wang, Mingli Song, Jie Song
PruningBench showcases the following three characteristics: 1) PruningBench employs a unified and consistent framework for evaluating the effectiveness of diverse structural pruning techniques; 2) PruningBench systematically evaluates 16 existing pruning methods, encompassing a wide array of models (e. g., CNNs and ViTs) and tasks (e. g., classification and detection); 3) PruningBench provides easily implementable interfaces to facilitate the implementation of future pruning methods, and enables the subsequent researchers to incorporate their work into our leaderboards.
1 code implementation • 18 Jun 2024 • Yuyan Liu, Sirui Ding, Sheng Zhou, Wenqi Fan, Qiaoyu Tan
Molecular property prediction (MPP) is a fundamental and crucial task in drug discovery.
2 code implementations • 17 Jun 2024 • Renqiu Xia, Song Mao, Xiangchao Yan, Hongbin Zhou, Bo Zhang, Haoyang Peng, Jiahao Pi, Daocheng Fu, Wenjie Wu, Hancheng Ye, Shiyang Feng, Chao Xu, Conghui He, Pinlong Cai, Min Dou, Botian Shi, Sheng Zhou, Yongwei Wang, Bin Wang, Junchi Yan, Fei Wu, Yu Qiao
Scientific documents record research findings and valuable human knowledge, comprising a vast corpus of high-quality data.
1 code implementation • 13 Jun 2024 • Zhiyao Zhou, Sheng Zhou, Bochao Mao, Jiawei Chen, Qingyun Sun, Yan Feng, Chun Chen, Can Wang
Notably, applying node-level GSL to graph classification is non-trivial due to the lack of find-grained guidance for intricate structure learning.
1 code implementation • 6 Jun 2024 • Zhonghao Wang, Danyu Sun, Sheng Zhou, Haobo Wang, Jiapei Fan, Longtao Huang, Jiajun Bu
However, due to variations in dataset selection, data splitting, and preprocessing techniques, the community currently lacks a comprehensive benchmark, which impedes deeper understanding and further development of GLN.
1 code implementation • 6 Jun 2024 • Chengyu Lai, Sheng Zhou, Zhimeng Jiang, Qiaoyu Tan, Yuanchen Bei, Jiawei Chen, Ningyu Zhang, Jiajun Bu
This paper introduces a novel and significant task termed recommendation editing, which focuses on modifying known and unsuitable recommendation behaviors.
no code implementations • 5 Jun 2024 • Sheng Zhou, Yukuan Jia, Ruiqing Mao, Zhaojun Nan, Yuxuan Sun, Zhisheng Niu
In this article, a task-oriented wireless communication framework is proposed to jointly optimize the communication scheme and the CP procedure.
no code implementations • 31 May 2024 • Zhuonan Zheng, Sheng Zhou, Hongjia Xu, Ming Gu, Yilun Xu, Ao Li, Yuhong Li, Jingjun Gu, Jiajun Bu
Both the homophilous and heterophilous patterns are propagated with a novel semantic-aware message passing mechanism.
Ranked #8 on
Node Classification
on Wisconsin
1 code implementation • 28 May 2024 • Zhuonan Zheng, Yuanchen Bei, Sheng Zhou, Yao Ma, Ming Gu, Hongjia Xu, Chengyu Lai, Jiawei Chen, Jiajun Bu
Based on HTMP and empirical analysis, we reveal that the success of message passing in existing HTGNNs is attributed to implicitly enhancing the compatibility matrix among classes.
no code implementations • 6 May 2024 • Qinyu Chen, Kwantae Kim, Chang Gao, Sheng Zhou, Taekwang Jang, Tobi Delbruck, Shih-Chii Liu
This paper introduces DeltaKWS, to the best of our knowledge, the first $\Delta$RNN-enabled fine-grained temporal sparsity-aware KWS IC for voice-controlled devices.
no code implementations • 25 Apr 2024 • Yuanchen Bei, Sheng Zhou, Jinke Shi, Yao Ma, Haishuai Wang, Jiajun Bu
Recent advances have utilized Graph Neural Networks (GNNs) to learn effective node representations by aggregating information from neighborhoods.
1 code implementation • 18 Apr 2024 • Sirui Chen, Jiawei Chen, Sheng Zhou, Bohao Wang, Shen Han, Chanfei Su, Yuqing Yuan, Can Wang
Integrating both positive and negative feedback to form a signed graph can lead to a more comprehensive understanding of user preferences.
no code implementations • 18 Apr 2024 • Siyi Lin, Chongming Gao, Jiawei Chen, Sheng Zhou, Binbin Hu, Yan Feng, Chun Chen, Can Wang
Building on these insights, we propose a novel debiasing strategy that leverages a spectral norm regularizer to penalize the magnitude of the principal singular value.
no code implementations • 22 Mar 2024 • Yukuan Jia, Jiawen Zhang, Shimeng Lu, Baokang Fan, Ruiqing Mao, Sheng Zhou, Zhisheng Niu
Environmental perception in Automated Valet Parking (AVP) has been a challenging task due to severe occlusions in parking garages.
1 code implementation • 20 Feb 2024 • Bohao Wang, Jiawei Chen, Changdong Li, Sheng Zhou, Qihao Shi, Yang Gao, Yan Feng, Chun Chen, Can Wang
DR-GNN addresses two core challenges: 1) To enable DRO to cater to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing regularizer, thereby facilitating the nuanced application of DRO; 2) Given the typically sparse nature of recommendation data, which might impede robust optimization, we introduce slight perturbations in the training distribution to expand its support.
1 code implementation • 8 Feb 2024 • Meihan Liu, Zeyu Fang, Zhen Zhang, Ming Gu, Sheng Zhou, Xin Wang, Jiajun Bu
Motivated by our empirical analysis, we reevaluate the role of GNNs in graph domain adaptation and uncover the pivotal role of the propagation process in GNNs for adapting to different graph domains.
no code implementations • 3 Feb 2024 • Hongjia Xu, Liangliang Zhang, Yao Ma, Sheng Zhou, Zhuonan Zheng, Bu Jiajun
Analytics on large-scale graphs have posed significant challenges to computational efficiency and resource requirements.
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.
no code implementations • 18 Jan 2024 • Tan Chen, Jintao Yan, Yuxuan Sun, Sheng Zhou, Deniz Gündüz, Zhisheng Niu
Hierarchical federated learning (HFL) enables distributed training of models across multiple devices with the help of several edge servers and a cloud edge server in a privacy-preserving manner.
no code implementations • 14 Dec 2023 • Xi Chen, Chang Gao, Zuowen Wang, Longbiao Cheng, Sheng Zhou, Shih-Chii Liu, Tobi Delbruck
Implementing online training of RNNs on the edge calls for optimized algorithms for an efficient deployment on hardware.
no code implementations • 9 Dec 2023 • Yuanchen Bei, Sheng Zhou, Qiaoyu Tan, Hao Xu, Hao Chen, Zhao Li, Jiajun Bu
To address these issues, we utilize the advantages of reinforcement learning in adaptively learning in complex environments and propose a novel method that incorporates Reinforcement neighborhood selection for unsupervised graph ANomaly Detection (RAND).
no code implementations • 13 Oct 2023 • Sheng Zhou, Dan Guo, Jia Li, Xun Yang, Meng Wang
The associations between these repetitive objects are superfluous for answer reasoning; (2) two spatially distant OCR tokens detected in the image frequently have weak semantic dependencies for answer reasoning; and (3) the co-existence of nearby objects and tokens may be indicative of important visual cues for predicting answers.
no code implementations • 22 Sep 2023 • Huixuan Chi, Hao Xu, Mengya Liu, Yuanchen Bei, Sheng Zhou, Danyang Liu, Mengdi Zhang
(2) spatiotemporal collaborative signal, which indicates similar users have similar preferences at specific locations and times.
1 code implementation • 13 Aug 2023 • Zijie Song, Jiawei Chen, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen, Can Wang
In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data.
1 code implementation • 10 Aug 2023 • Ming Gu, Gaoming Yang, Sheng Zhou, Ning Ma, Jiawei Chen, Qiaoyu Tan, Meihan Liu, Jiajun Bu
Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results.
1 code implementation • 9 Aug 2023 • Chunpeng Zhou, Kangjie Ning, Haishuai Wang, Zhi Yu, Sheng Zhou, Jiajun Bu
To address these challenges, we introduce a novel methodology for the subgrade distress detection task by leveraging the multi-view information from 3D-GPR data.
no code implementations • 18 Jul 2023 • Xiufeng Huang, Sheng Zhou
To improve the performance of multi-agent reinforcement learning under the constraint of wireless resources, we propose a message importance metric and design an importance-aware scheduling policy to effectively exchange messages.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
no code implementations • 6 Jul 2023 • Yuanchen Bei, Hao Xu, Sheng Zhou, Huixuan Chi, Haishuai Wang, Mengdi Zhang, Zhao Li, Jiajun Bu
Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world.
no code implementations • 19 Jun 2023 • Tan Chen, Jintao Yan, Yuxuan Sun, Sheng Zhou, Deniz Gunduz, Zhisheng Niu
Federated learning enables distributed training of machine learning (ML) models across multiple devices in a privacy-preserving manner.
1 code implementation • NeurIPS 2023 • Zhiyao Zhou, Sheng Zhou, Bochao Mao, Xuanyi Zhou, Jiawei Chen, Qiaoyu Tan, Daochen Zha, Yan Feng, Chun Chen, Can Wang
Moreover, we observe that the learned graph structure demonstrates a strong generalization ability across different GNN models, despite the high computational and space consumption.
no code implementations • 25 Feb 2023 • Yukuan Jia, Ruiqing Mao, Yuxuan Sun, Sheng Zhou, Zhisheng Niu
Specifically, we design a mobility-aware sensor scheduling (MASS) algorithm based on the restless multi-armed bandit (RMAB) theory to maximize the expected average perception gain.
no code implementations • 20 Feb 2023 • Sheng Zhou, Pierre Blanchart, Michel Crucianu, Marin Ferecatu
In this paper we present a heuristic method to provide individual explanations for those elements in a dataset (data points) which are wrongly predicted by a given classifier.
2 code implementations • 9 Feb 2023 • Jiawei Chen, Junkang Wu, Jiancan Wu, Sheng Zhou, Xuezhi Cao, Xiangnan He
Recent years have witnessed the great successes of embedding-based methods in recommender systems.
no code implementations • 30 Jan 2023 • Yaodan Xu, Jingzhou Sun, Sheng Zhou, Zhisheng Niu
In particular, parallel computing resources on the platforms, such as graphics processing units (GPUs), have higher computational and energy efficiency with larger batch sizes.
no code implementations • 28 Dec 2022 • Qihao Shi, Bingyang Fu, Can Wang, Jiawei Chen, Sheng Zhou, Yan Feng, Chun Chen
The approximation ratio of the algorithm depends both on the number of the removed elements and the network topology.
no code implementations • 7 Dec 2022 • Bowen Xie, Yuxuan Sun, Sheng Zhou, Zhisheng Niu, Yang Xu, Jingran Chen, Deniz Gündüz
Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and communication capabilities.
1 code implementation • 27 Nov 2022 • Gang Chen, Jiawei Chen, Fuli Feng, Sheng Zhou, Xiangnan He
Traditional solutions first train a full teacher model from the training data, and then transfer its knowledge (\ie \textit{soft labels}) to supervise the learning of a compact student model.
1 code implementation • 8 Nov 2022 • Dian Qin, Haishuai Wang, Zhe Liu, Hongjia Xu, Sheng Zhou, Jiajun Bu
Since the distilled 2D networks are supervised by the curves converted from dimensionally heterogeneous 3D features, the 2D networks are given an informative view in terms of learning structural information embedded in well-trained high-dimensional representations.
no code implementations • 27 Oct 2022 • Yuxuan Sun, Bowen Xie, Sheng Zhou, Zhisheng Niu
Accordingly, base stations (BSs) and edge servers (ESs) need to be densely deployed, leading to huge deployment and operation costs, in particular the energy costs.
1 code implementation • 29 Aug 2022 • Jingru Li, Sheng Zhou, Liangcheng Li, Haishuai Wang, Zhi Yu, Jiajun Bu
Besides, CuDFKD adapts the generation target dynamically according to the status of student model.
1 code implementation • 15 Jul 2022 • Ruiqing Mao, Jingyu Guo, Yukuan Jia, Yuxuan Sun, Sheng Zhou, Zhisheng Niu
In this work, we release DOLPHINS: Dataset for cOllaborative Perception enabled Harmonious and INterconnected Self-driving, as a new simulated large-scale various-scenario multi-view multi-modality autonomous driving dataset, which provides a ground-breaking benchmark platform for interconnected autonomous driving.
1 code implementation • 15 Jun 2022 • Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao Li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, Martin Ester
Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches.
no code implementations • 3 Jun 2022 • Wenqi Shi, Sheng Zhou, Zhisheng Niu, Miao Jiang, Lu Geng
To deal with the coupled offloading and scheduling introduced by concurrent batch processing, we first consider an offline problem with a constant edge inference latency and the same latency constraint.
2 code implementations • 24 Apr 2022 • Chao Lin, Zhao Li, Sheng Zhou, Shichang Hu, Jialun Zhang, Linhao Luo, Jiarun Zhang, Longtao Huang, Yuan He
Virtual try-on(VTON) aims at fitting target clothes to reference person images, which is widely adopted in e-commerce. Existing VTON approaches can be narrowly categorized into Parser-Based(PB) and Parser-Free(PF) by whether relying on the parser information to mask the persons' clothes and synthesize try-on images.
no code implementations • 17 Feb 2022 • Yuxuan Sun, Sheng Zhou, Zhisheng Niu, Deniz Gündüz
In this work, we propose time-correlated sparsification with hybrid aggregation (TCS-H) for communication-efficient FEEL, which exploits jointly the power of model compression and over-the-air computation.
no code implementations • 12 Feb 2022 • Yukuan Jia, Ruiqing Mao, Yuxuan Sun, Sheng Zhou, Zhisheng Niu
Cooperative perception of connected vehicles comes to the rescue when the field of view restricts stand-alone intelligence.
1 code implementation • 28 Dec 2021 • Can Wang, Zhe Wang, Defang Chen, Sheng Zhou, Yan Feng, Chun Chen
Knowledge distillation, a technique recently gaining popularity for enhancing model generalization in Convolutional Neural Networks (CNNs), operates under the assumption that both teacher and student models are trained on identical data distributions.
no code implementations • 23 Sep 2021 • Yuxuan Sun, Fan Zhang, Junlin Zhao, Sheng Zhou, Zhisheng Niu, Deniz Gündüz
In this work, we consider a multi-master heterogeneous-worker distributed computing scenario, where multiple matrix multiplication tasks are encoded and allocated to workers for parallel computation.
no code implementations • 16 Sep 2021 • Zihao Zhao, Jiawei Chen, Sheng Zhou, Xiangnan He, Xuezhi Cao, Fuzheng Zhang, Wei Wu
To sufficiently exploit such important information for recommendation, it is essential to disentangle the benign popularity bias caused by item quality from the harmful popularity bias caused by conformity.
1 code implementation • 23 Aug 2021 • Dian Qin, Jiajun Bu, Zhe Liu, Xin Shen, Sheng Zhou, Jingjun Gu, Zhijua Wang, Lei Wu, Huifen Dai
To deal with this problem, we propose an efficient architecture by distilling knowledge from well-trained medical image segmentation networks to train another lightweight network.
1 code implementation • ICCV 2021 • Sheng Zhou, Yucheng Wang, Defang Chen, Jiawei Chen, Xin Wang, Can Wang, Jiajun Bu
The holistic knowledge is represented as a unified graph-based embedding by aggregating individual knowledge from relational neighborhood samples with graph neural networks, the student network is learned by distilling the holistic knowledge in a contrastive manner.
no code implementations • 14 Jul 2021 • Ning Ma, Jiajun Bu, Zhen Zhang, Sheng Zhou
Present domain adaptation methods usually perform explicit representation alignment by simultaneously accessing the source data and target data.
no code implementations • 14 Jul 2021 • Ning Ma, Jiajun Bu, Lixian Lu, Jun Wen, Zhen Zhang, Sheng Zhou, Xifeng Yan
Domain Adaptation has been widely used to deal with the distribution shift in vision, language, multimedia etc.
no code implementations • 31 May 2021 • Yuxuan Sun, Sheng Zhou, Zhisheng Niu, Deniz Gündüz
In this work, we consider an over-the-air FEEL system with analog gradient aggregation, and propose an energy-aware dynamic device scheduling algorithm to optimize the training performance under energy constraints of devices, where both communication energy for gradient aggregation and computation energy for local training are included.
no code implementations • 16 Nov 2020 • Jiawei Chen, Chengquan Jiang, Can Wang, Sheng Zhou, Yan Feng, Chun Chen, Martin Ester, Xiangnan He
To deal with these problems, we propose an efficient and effective collaborative sampling method CoSam, which consists of: (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency; and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling.
1 code implementation • 16 Nov 2020 • Can Wang, Jiawei Chen, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen
However, the social network information may not be available in many recommender systems, which hinders application of SamWalker.
no code implementations • 3 Aug 2020 • Ruichen Jiang, Sheng Zhou
To mitigate wireless fading, we further propose a cluster-based system and design the relay selection scheme based on the normalized detection SNR.
no code implementations • 14 Jul 2020 • Wenqi Shi, Sheng Zhou, Zhisheng Niu, Miao Jiang, Lu Geng
Then, a greedy device scheduling algorithm is introduced, which in each step selects the device consuming the least updating time obtained by the optimal bandwidth allocation, until the lower bound begins to increase, meaning that scheduling more devices will degrade the model accuracy.
no code implementations • 25 May 2020 • Xiufeng Huang, Sheng Zhou
To reduce the communication cost, lossy data compression can be exploited for inference tasks, but may bring more erroneous inference results.
no code implementations • 18 Mar 2020 • Ning Ma, Jiajun Bu, Jieyu Yang, Zhen Zhang, Chengwei Yao, Zhi Yu, Sheng Zhou, Xifeng Yan
The shared sub-structures between training classes and test classes are essential in few-shot graph classification.
no code implementations • 4 Mar 2020 • Jiawei Chen, Can Wang, Sheng Zhou, Qihao Shi, Jingbang Chen, Yan Feng, Chun Chen
A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence.
no code implementations • 30 Jan 2020 • Sheng Zhou, Xinjiang Wang, Ping Luo, Litong Feng, Wenjie Li, Wei zhang
This phenomenon is caused by the normalization effect of BN, which induces a non-trainable region in the parameter space and reduces the network capacity as a result.
no code implementations • 3 Nov 2019 • Wenqi Shi, Sheng Zhou, Zhisheng Niu
In each iteration of FL (called round), the edge devices update local models based on their own data and contribute to the global training by uploading the model updates via wireless channels.
no code implementations • 1 Nov 2019 • Yuxuan Sun, Sheng Zhou, Deniz Gündüz
In this work, we consider analog aggregation to scale down the communication cost with respect to the number of workers, and introduce data redundancy to the system to deal with non-i. i. d.
1 code implementation • 8 Mar 2019 • Wenqi Shi, Yunzhong Hou, Sheng Zhou, Zhisheng Niu, Yang Zhang, Lu Geng
Since the output data size of a DNN layer can be larger than that of the raw data, offloading intermediate data between layers can suffer from high transmission latency under limited wireless bandwidth.
no code implementations • 6 Mar 2019 • Zhiyuan Jiang, Sheng Zhou, Zhisheng Niu
Future wireless access networks need to support diversified quality of service (QoS) metrics required by various types of Internet-of-Things (IoT) devices, e. g., age of information (AoI) for status generating sources and ultra low latency for safety information in vehicular networks.
2 code implementations • 31 Jan 2019 • Sheng Zhou, Jiajun Bu, Xin Wang, Jia-Wei Chen, Can Wang
Second, given a meta path, nodes in HIN are connected by path instances while existing works fail to fully explore the differences between path instances that reflect nodes' preferences in the semantic space.
no code implementations • 4 Dec 2018 • Ruichen Deng, Zhiyuan Jiang, Sheng Zhou, Shuguang Cui, Zhisheng Niu
Timely and accurate knowledge of channel state information (CSI) is necessary to support scheduling operations at both physical and network layers.
no code implementations • 4 Dec 2018 • Sheng Chen, Zhiyuan Jiang, Sheng Zhou, Zhisheng Niu
In this paper, we propose a learning-based low-overhead beam alignment method for vehicle-to-infrastructure communication in vehicular networks.
no code implementations • 4 Dec 2018 • Zhiyuan Jiang, Ziyan He, Sheng Chen, Andreas F. Molisch, Sheng Zhou, Zhisheng Niu
Channel state information (CSI) is of vital importance in wireless communication systems.
no code implementations • 3 Dec 2018 • Zhiyuan Jiang, Sheng Chen, Andreas F. Molisch, Rath Vannithamby, Sheng Zhou, Zhisheng Niu
Knowledge of information about the propagation channel in which a wireless system operates enables better, more efficient approaches for signal transmissions.
no code implementations • 17 Nov 2015 • Huimin Pan, Jingchu Liu, Sheng Zhou, Zhisheng Niu
Based on these characteristics, we propose a \emph{Block Regression} ({BR}) model for mobile traffic forecasting.