1 code implementation • ACL 2022 • Yongqi Zhang, Zhanke Zhou, Quanming Yao, Yong Li
Based on the analysis, we propose an efficient two-stage search algorithm KGTuner, which efficiently explores HP configurations on small subgraph at the first stage and transfers the top-performed configurations for fine-tuning on the large full graph at the second stage.
no code implementations • 16 Mar 2024 • Xiaochong Lan, Yiming Cheng, Li Sheng, Chen Gao, Yong Li
Depression detection aims to determine whether an individual suffers from depression by analyzing their history of posts on social media, which can significantly aid in early detection and intervention.
no code implementations • 6 Mar 2024 • Yong Li, Shiguang Shan
We formulate the self-supervised AU representation learning signals in two-fold: (1) AU representation should be frame-wisely discriminative within a short video clip; (2) Facial frames sampled from different identities but show analogous facial AUs should have consistent AU representations.
no code implementations • 1 Mar 2024 • Jinzhu Mao, Dongyun Zou, Li Sheng, Siyi Liu, Chen Gao, Yue Wang, Yong Li
Identifying critical nodes in networks is a classical decision-making task, and many methods struggle to strike a balance between adaptability and utility.
1 code implementation • 29 Feb 2024 • Xingchen Zou, Yibo Yan, Xixuan Hao, Yuehong Hu, Haomin Wen, Erdong Liu, Junbo Zhang, Yong Li, Tianrui Li, Yu Zheng, Yuxuan Liang
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e. g., geographical, traffic, social media, and environmental data) and modalities (e. g., spatio-temporal, visual, and textual modalities).
no code implementations • 27 Feb 2024 • Zhilun Zhou, Yuming Lin, Depeng Jin, Yong Li
To deal with the different facilities needs of residents, we initiate a discussion among the residents in each community about the plan, where residents provide feedback based on their profiles.
no code implementations • 23 Feb 2024 • Jingtao Ding, Chang Liu, Yu Zheng, Yunke Zhang, Zihan Yu, Ruikun Li, Hongyi Chen, Jinghua Piao, Huandong Wang, Jiazhen Liu, Yong Li
Complex networks pervade various real-world systems, from the natural environment to human societies.
no code implementations • 21 Feb 2024 • Shutong Qiao, Chen Gao, Junhao Wen, Wei Zhou, Qun Luo, Peixuan Chen, Yong Li
However, constrained by high time and space costs, as well as the brief and anonymous nature of session data, the first LLM recommendation framework suitable for industrial deployment has yet to emerge in the field of SBR.
1 code implementation • 19 Feb 2024 • Yuan Yuan, Chenyang Shao, Jingtao Ding, Depeng Jin, Yong Li
To bridge this gap, we propose a novel generative pre-training framework, GPDiff, for STG transfer learning.
no code implementations • 19 Feb 2024 • Yuan Yuan, Jingtao Ding, Jie Feng, Depeng Jin, Yong Li
Urban spatio-temporal prediction is crucial for informed decision-making, such as transportation management, resource optimization, and urban planning.
no code implementations • 18 Feb 2024 • Lin Chen, Fengli Xu, Nian Li, Zhenyu Han, Meng Wang, Yong Li, Pan Hui
We propose a novel REasoning meta-STRUCTure search (ReStruct) framework that integrates LLM reasoning into the evolutionary procedure.
no code implementations • 15 Feb 2024 • Chenyang Shao, Fengli Xu, Bingbing Fan, Jingtao Ding, Yuan Yuan, Meng Wang, Yong Li
In this paper, we design a novel Mobility Generation as Reasoning (MobiGeaR) framework that prompts LLM to recursively generate mobility behaviour.
no code implementations • 10 Feb 2024 • Yongqing Xu, Yong Li, Tony Q. S. Quek
Cognitive radio (CR) and integrated sensing and communication (ISAC) are both critical technologies for the sixth generation (6G) wireless networks.
1 code implementation • 8 Feb 2024 • Hongyi Chen, Jingtao Ding, Yong Li, Yue Wang, Xiao-Ping Zhang
In this paper, we propose a social physics-informed diffusion model named SPDiff to mitigate the above gap.
1 code implementation • 7 Feb 2024 • Jinwei Zeng, Yu Liu, Jingtao Ding, Jian Yuan, Yong Li
To relieve this issue by utilizing the strong pattern recognition of artificial intelligence, we incorporate two sources of open data representative of the transportation demand and capacity factors, the origin-destination (OD) flow data and the road network data, to build a hierarchical heterogeneous graph learning method for on-road carbon emission estimation (HENCE).
no code implementations • 4 Feb 2024 • Yu Shang, Yu Li, Fengli Xu, Yong Li
Previous works like chain-of-thought (CoT) and tree-of-thoughts(ToT) have predominately focused on enhancing accuracy, but overlook the rapidly increasing token cost, which could be particularly problematic for open-ended real-world tasks with huge solution spaces.
no code implementations • 2 Feb 2024 • Siyi Liu, Chen Gao, Yong Li
Hyperparameter optimization is critical in modern machine learning, requiring expert knowledge, numerous trials, and high computational and human resources.
no code implementations • 24 Jan 2024 • Zhilun Zhou, Yuming Lin, Yong Li
Participatory urban planning is the mainstream of modern urban planning and involves the active engagement of different stakeholders.
no code implementations • 24 Jan 2024 • Dong Han, Yong Li, Joachim Denzler
Lastly, secure multiparty computation is implemented for safely computing the embedding distance during model inference.
1 code implementation • 16 Jan 2024 • Xin Zhang, Yu Liu, Yuming Lin, Qingmin Liao, Yong Li
Urban villages, defined as informal residential areas in or around urban centers, are characterized by inadequate infrastructures and poor living conditions, closely related to the Sustainable Development Goals (SDGs) on poverty, adequate housing, and sustainable cities.
1 code implementation • 19 Dec 2023 • Fengli Xu, Jun Zhang, Chen Gao, Jie Feng, Yong Li
Urban environments, characterized by their complex, multi-layered networks encompassing physical, social, economic, and environmental dimensions, face significant challenges in the face of rapid urbanization.
no code implementations • 19 Dec 2023 • Jie Liu, Yijia Cao, Yong Li, Yixiu Guo, Wei Deng
Accurately predicting line loss rates is vital for effective line loss management in distribution networks, especially over short-term multi-horizons ranging from one hour to one week.
no code implementations • 19 Dec 2023 • Chen Gao, Xiaochong Lan, Nian Li, Yuan Yuan, Jingtao Ding, Zhilun Zhou, Fengli Xu, Yong Li
Finally, since this area is new and quickly evolving, we discuss the open problems and promising future directions.
1 code implementation • 13 Dec 2023 • Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li
The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance.
no code implementations • 13 Dec 2023 • Huan Yan, Yong Li
Intelligent transportation systems play a crucial role in modern traffic management and optimization, greatly improving traffic efficiency and safety.
1 code implementation • 14 Nov 2023 • GuanYu Lin, Chen Gao, Yu Zheng, Jianxin Chang, Yanan Niu, Yang song, Kun Gai, Zhiheng Li, Depeng Jin, Yong Li, Meng Wang
Recent proposed cross-domain sequential recommendation models such as PiNet and DASL have a common drawback relying heavily on overlapped users in different domains, which limits their usage in practical recommender systems.
1 code implementation • 14 Nov 2023 • GuanYu Lin, Chen Gao, Yu Zheng, Yinfeng Li, Jianxin Chang, Yanan Niu, Yang song, Kun Gai, Zhiheng Li, Depeng Jin, Yong Li
In this paper, we propose a meta-learning method to annotate the unlabeled data from loss and gradient perspectives, which considers the noises in both positive and negative instances.
no code implementations • 10 Nov 2023 • Wenjie Fu, Huandong Wang, Chen Gao, Guanghua Liu, Yong Li, Tao Jiang
Prior attempts have quantified the privacy risks of language models (LMs) via MIAs, but there is still no consensus on whether existing MIA algorithms can cause remarkable privacy leakage on practical Large Language Models (LLMs).
1 code implementation • 9 Nov 2023 • Zhenyu Han, Yanxin Xi, Tong Xia, Yu Liu, Yong Li
Built environment supports all the daily activities and shapes our health.
no code implementations • 4 Nov 2023 • Yong Li, Zhiguo Zhao, Yunli Chen, Rui Tian
To address these challenges, our research introduces a parallel spatial transformation (PST)-based framework for large-scale, multi-view, multi-sensor scenarios.
no code implementations • 30 Oct 2023 • Huiyao Shu, Ang Wang, Ziji Shi, Hanyu Zhao, Yong Li, Lu Lu
However, a memory-efficient execution plan that includes a reasonable operator execution order and tensor memory layout can significantly increase the models' memory efficiency and reduce overheads from high-level techniques.
no code implementations • 16 Oct 2023 • Nian Li, Chen Gao, Yong Li, Qingmin Liao
In this work, we take an early step in introducing a novel approach that leverages LLMs in macroeconomic simulation.
no code implementations • 16 Oct 2023 • Xiaochong Lan, Chen Gao, Depeng Jin, Yong Li
Next, in the reasoning-enhanced debating stage, for each potential stance, we designate a specific LLM-based agent to advocate for it, guiding the LLM to detect logical connections between text features and stance, tackling the second challenge.
2 code implementations • 13 Oct 2023 • Ling Yue, Yongqi Zhang, Quanming Yao, Yong Li, Xian Wu, Ziheng Zhang, Zhenxi Lin, Yefeng Zheng
Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways.
Ranked #1 on Link Property Prediction on ogbl-biokg
no code implementations • 3 Oct 2023 • Xuanming Hu, Wei Fan, Dongjie Wang, Pengyang Wang, Yong Li, Yanjie Fu
We design several experiments to indicate that our framework can outperform compared to other generative models for the urban planning task.
1 code implementation • NeurIPS 2023 • Haoran Ye, Jiarui Wang, Zhiguang Cao, Helan Liang, Yong Li
As a Neural Combinatorial Optimization method, DeepACO performs better than or on par with problem-specific methods on canonical routing problems.
1 code implementation • 19 Sep 2023 • Zhilun Zhou, Jingtao Ding, Yu Liu, Depeng Jin, Yong Li
To capture the effect of multiple factors on urban flow, such as region features and urban environment, we employ diffusion model to generate urban flow for regions under different conditions.
1 code implementation • 19 Sep 2023 • Haojun Xia, Zhen Zheng, Yuchao Li, Donglin Zhuang, Zhongzhu Zhou, Xiafei Qiu, Yong Li, Wei Lin, Shuaiwen Leon Song
Therefore, we propose Flash-LLM for enabling low-cost and highly-efficient large generative model inference with the sophisticated support of unstructured sparsity on high-performance but highly restrictive Tensor Cores.
no code implementations • 28 Aug 2023 • Yuhan Quan, Jingtao Ding, Chen Gao, Nian Li, Lingling Yi, Depeng Jin, Yong Li
Micro-videos platforms such as TikTok are extremely popular nowadays.
no code implementations • 26 Aug 2023 • Jian Zhu, Wen Cheng, Yu Cui, Chang Tang, Yuyang Dai, Yong Li, Lingfang Zeng
Hash representation learning of multi-view heterogeneous data is the key to improving the accuracy of multimedia retrieval.
no code implementations • 25 Aug 2023 • Yunzhu Pan, Nian Li, Chen Gao, Jianxin Chang, Yanan Niu, Yang song, Depeng Jin, Yong Li
Specifically, in short-video recommendation, the easiest-to-collect user feedback is the skipping behavior, which leads to two critical challenges for the recommendation model.
no code implementations • 23 Aug 2023 • Wenjie Fu, Huandong Wang, Chen Gao, Guanghua Liu, Yong Li, Tao Jiang
Membership Inference Attack (MIA) identifies whether a record exists in a machine learning model's training set by querying the model.
no code implementations • 17 Aug 2023 • Yuanzhi Wang, Yong Li, Xiaoya Zhang, Xin Liu, Anbo Dai, Antoni B. Chan, Zhen Cui
In addition to the utilization of a pretrained T2I 2D Unet for spatial content manipulation, we establish a dedicated temporal Unet architecture to faithfully capture the temporal coherence of the input video sequences.
no code implementations • 8 Aug 2023 • Yunzhu Pan, Chen Gao, Jianxin Chang, Yanan Niu, Yang song, Kun Gai, Depeng Jin, Yong Li
To enhance the robustness of our model, we then introduce a multi-task learning module to simultaneously optimize two kinds of feedback -- passive-negative feedback and traditional randomly-sampled negative feedback.
no code implementations • 7 Aug 2023 • Taichi Liu, Chen Gao, Zhenyu Wang, Dong Li, Jianye Hao, Depeng Jin, Yong Li
Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems.
1 code implementation • 5 Aug 2023 • Yuhao Dan, Zhikai Lei, Yiyang Gu, Yong Li, Jianghao Yin, Jiaju Lin, Linhao Ye, Zhiyan Tie, Yougen Zhou, Yilei Wang, Aimin Zhou, Ze Zhou, Qin Chen, Jie zhou, Liang He, Xipeng Qiu
Currently, EduChat is available online as an open-source project, with its code, data, and model parameters available on platforms (e. g., GitHub https://github. com/icalk-nlp/EduChat, Hugging Face https://huggingface. co/ecnu-icalk ).
1 code implementation • 1 Aug 2023 • Yanxin Xi, Yu Liu, Tong Li, Jintao Ding, Yunke Zhang, Sasu Tarkoma, Yong Li, Pan Hui
Especially satellite imagery is a potential data source for studying sustainable urban development.
no code implementations • 31 Jul 2023 • Xiaochong Lan, Chen Gao, Shiqi Wen, Xiuqi Chen, Yingge Che, Han Zhang, Huazhou Wei, Hengliang Luo, Yong Li
To address these two challenges, we design a system of living NEeds predictiON named NEON, consisting of three phases: feature mining, feature fusion, and multi-task prediction.
1 code implementation • 19 Jul 2023 • Feiran Hu, Peng Wang, Yangyang Li, Chenlong Duan, Zijian Zhu, Fei Wang, Faen Zhang, Yong Li, Xiu-Shen Wei
The SnakeCLEF2023 competition aims to the development of advanced algorithms for snake species identification through the analysis of images and accompanying metadata.
1 code implementation • 19 Jul 2023 • Jinzhu Mao, Liu Cao, Chen Gao, Huandong Wang, Hangyu Fan, Depeng Jin, Yong Li
Understanding and characterizing the vulnerability of urban infrastructures, which refers to the engineering facilities essential for the regular running of cities and that exist naturally in the form of networks, is of great value to us.
1 code implementation • 12 Jul 2023 • Yan Wen, Chen Gao, Lingling Yi, Liwei Qiu, Yaqing Wang, Yong Li
Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner.
no code implementations • 3 Jul 2023 • Xinhang Li, Xiangyu Zhao, Yejing Wang, Yu Liu, Yong Li, Cheng Long, Yong Zhang, Chunxiao Xing
As a representative information retrieval task, site recommendation, which aims at predicting the optimal sites for a brand or an institution to open new branches in an automatic data-driven way, is beneficial and crucial for brand development in modern business.
no code implementations • 17 Jun 2023 • Huandong Wang, Huan Yan, Can Rong, Yuan Yuan, Fenyu Jiang, Zhenyu Han, Hongjie Sui, Depeng Jin, Yong Li
In this survey, we will systematically review the literature on multi-scale simulation of complex systems from the perspective of knowledge and data.
no code implementations • 14 Jun 2023 • Tong Li, Li Yu, Yibo Ma, Tong Duan, Wenzhen Huang, Yan Zhou, Depeng Jin, Yong Li, Tao Jiang
We show that the decline in carbon efficiency leads to a carbon efficiency trap, estimated to cause additional carbon emissions of 23. 82 +- 1. 07 megatons in China.
no code implementations • 8 Jun 2023 • Can Rong, Jingtao Ding, Zhicheng Liu, Yong Li
The Origin-Destination~(OD) networks provide an estimation of the flow of people from every region to others in the city, which is an important research topic in transportation, urban simulation, etc.
no code implementations • 6 Jun 2023 • Can Rong, Huandong Wang, Yong Li
Origin-destination (OD) flow, which contains valuable population mobility information including direction and volume, is critical in many urban applications, such as urban planning, transportation management, etc.
1 code implementation • 24 May 2023 • Jiajia Chen, Jiancan Wu, Jiawei Chen, Xin Xin, Yong Li, Xiangnan He
Through theoretical analyses, we identify two fundamental factors: (1) with graph convolution (\textit{i. e.,} neighborhood aggregation), popular items exert larger influence than tail items on neighbor users, making the users move towards popular items in the representation space; (2) after multiple times of graph convolution, popular items would affect more high-order neighbors and become more influential.
1 code implementation • 22 May 2023 • Yu Zheng, Hongyuan Su, Jingtao Ding, Depeng Jin, Yong Li
Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums, or yield sub-optimal road plans in terms of accessibility and construction costs.
2 code implementations • 21 May 2023 • Yuan Yuan, Jingtao Ding, Chenyang Shao, Depeng Jin, Yong Li
To enhance the learning of each step, an elaborated spatio-temporal co-attention module is proposed to capture the interdependence between the event time and space adaptively.
no code implementations • 15 May 2023 • Suguman Bansal, Yong Li, Lucas Martinelli Tabajara, Moshe Y. Vardi, Andrew Wells
Our central result is that LTLf model checking of non-terminating transducers is \emph{exponentially harder} than that of terminating transducers.
no code implementations • 10 May 2023 • Wang-Yu Tong, Yong Li, Shou-Dong Ye, An-Jing Wang, Yan-Yan Tang, Mei-Li Li, Zhong-Fan Yu, Ting-Ting Xia, Qing-Yang Liu, Si-Qi Zhu
RNA-guided gene editing based on the CRISPR-Cas system is currently the most effective genome editing technique.
1 code implementation • 6 Apr 2023 • Yu Zhang, Xiaoguang Di, Junde Wu, Rao Fu, Yong Li, Yue Wang, Yanwu Xu, Guohui YANG, Chunhui Wang
In this paper, to make the learning easier in low-light image enhancement, we introduce FLW-Net (Fast and LightWeight Network) and two relative loss functions.
1 code implementation • CVPR 2023 • Yong Li, Yuanzhi Wang, Zhen Cui
Specially, the representation of each modality is decoupled into two parts, i. e., modality-irrelevant/-exclusive spaces, in a self-regression manner.
no code implementations • 22 Mar 2023 • Haiquan Qiu, Yongqi Zhang, Yong Li, Quanming Yao
Our results first show that GNN can capture logical rules from graded modal logic, providing a new theoretical tool for analyzing the expressiveness of GNN for KG reasoning; and a query labeling trick makes it easier for GNN to capture logical rules, explaining why SOTA methods are mainly based on labeling trick.
1 code implementation • 15 Mar 2023 • Yuhan Quan, Jingtao Ding, Chen Gao, Lingling Yi, Depeng Jin, Yong Li
Graph Neural Network(GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations.
no code implementations • 3 Mar 2023 • Yongqing Xu, Yong Li, J. Andrew Zhang, Marco Di Renzo, Tony Q. S. Quek
However, due to multiple performance metrics used for communication and sensing, the limited degrees-of-freedom (DoF) in optimizing ISAC systems poses a challenge.
1 code implementation • 25 Feb 2023 • Yu Liu, Xin Zhang, Jingtao Ding, Yanxin Xi, Yong Li
To address such issues, in this paper, we propose a Knowledge-infused Contrastive Learning (KnowCL) model for urban imagery-based socioeconomic prediction.
no code implementations • 22 Feb 2023 • Huiming Chen, Huandong Wang, Qingyue Long, Depeng Jin, Yong Li
Based on these frameworks, we have instantiated FedOpt algorithms.
1 code implementation • 9 Feb 2023 • Yuan Yuan, Huandong Wang, Jingtao Ding, Depeng Jin, Yong Li
To enhance the fidelity and utility of the generated activity data, our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model.
1 code implementation • 8 Feb 2023 • GuanYu Lin, Chen Gao, Yu Zheng, Jianxin Chang, Yanan Niu, Yang song, Zhiheng Li, Depeng Jin, Yong Li
In this paper, we propose Dual-interest Factorization-heads Attention for Sequential Recommendation (short for DFAR) consisting of feedback-aware encoding layer, dual-interest disentangling layer and prediction layer.
no code implementations • 1 Feb 2023 • Ziji Shi, Le Jiang, Ang Wang, Jie Zhang, Xianyan Jia, Yong Li, Chencan Wu, Jialin Li, Wei Lin
However, finding a suitable model parallel schedule for an arbitrary neural network is a non-trivial task due to the exploding search space.
1 code implementation • 2 Jan 2023 • Pengfei Wen, Zhi-Sheng Ye, Yong Li, Shaowei Chen, Pu Xie, Shuai Zhao
Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models.
no code implementations • CVPR 2023 • Wei Huang, Chang Chen, Yong Li, Jiacheng Li, Cheng Li, Fenglong Song, Youliang Yan, Zhiwei Xiong
In contrast to existing methods, we instead utilize the difference between images to build a better representation space, where the distinct style features are extracted and stored as the bases of representation.
1 code implementation • ICCV 2023 • Yuanzhi Wang, Zhen Cui, Yong Li
Recovering missed modality is popular in incomplete multimodal learning because it usually benefits downstream tasks.
no code implementations • 6 Nov 2022 • Zhen Cheng, Tao Wang, Yong Li, Fenglong Song, Chang Chen, Zhiwei Xiong
To solve this problem, we propose a learning-based data synthesis approach to learn the properties of real-world SDRTVs by integrating several tone mapping priors into both network structures and loss functions.
no code implementations • AAAI -22 2022 • Zefang Zong, Meng Zheng, Yong Li, Depeng Jin
It is of great importance to efficiently provide high-quality solutions of cooperative PDP.
1 code implementation • 11 Oct 2022 • Taolin Zhang, Junwei DOng, Jianing Wang, Chengyu Wang, Ang Wang, Yinghui Liu, Jun Huang, Yong Li, Xiaofeng He
Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge graphs, and/or linguistic knowledge from syntactic or dependency analysis.
1 code implementation • 18 Sep 2022 • GuanYu Lin, Chen Gao, Yinfeng Li, Yu Zheng, Zhiheng Li, Depeng Jin, Dong Li, Jianye Hao, Yong Li
Such user-centric recommendation will make it impossible for the provider to expose their new items, failing to consider the accordant interactions between user and item dimensions.
no code implementations • 17 Sep 2022 • Xiaocong Chen, Siyu Wang, Lina Yao, Lianyong Qi, Yong Li
It is more challenging to balance the exploration and exploitation in DRL RS where RS agent need to deeply explore the informative trajectories and exploit them efficiently in the context of recommender systems.
1 code implementation • 26 Aug 2022 • Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He, Yong Li
Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction.
1 code implementation • 14 Aug 2022 • Yinfeng Li, Chen Gao, Quanming Yao, Tong Li, Depeng Jin, Yong Li
In particular, we first unify the fine-grained user similarity and the complex matching between user preferences and spatiotemporal activity into a heterogeneous hypergraph.
1 code implementation • 10 Aug 2022 • Yu Zheng, Chen Gao, Jingtao Ding, Lingling Yi, Depeng Jin, Yong Li, Meng Wang
Recommender systems are prone to be misled by biases in the data.
no code implementations • 8 Aug 2022 • Zhilong Chen, Jinghua Piao, Xiaochong Lan, Hancheng Cao, Chen Gao, Zhicong Lu, Yong Li
Recommender systems are playing an increasingly important role in alleviating information overload and supporting users' various needs, e. g., consumption, socialization, and entertainment.
2 code implementations • 5 May 2022 • Yongqi Zhang, Zhanke Zhou, Quanming Yao, Yong Li
While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently.
no code implementations • 29 Apr 2022 • Xiaoxiao Xu, Zhiwei Fang, Qian Yu, Ruoran Huang, \\Chaosheng Fan, Yong Li, Yang He, Changping Peng, Zhangang Lin, Jingping Shao
The exposure sequence is being actively studied for user interest modeling in Click-Through Rate (CTR) prediction.
1 code implementation • 26 Apr 2022 • Jie Shuai, Kun Zhang, Le Wu, Peijie Sun, Richang Hong, Meng Wang, Yong Li
Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better?
1 code implementation • 11 Apr 2022 • Yuanxing Zhang, Langshi Chen, Siran Yang, Man Yuan, Huimin Yi, Jie Zhang, Jiamang Wang, Jianbo Dong, Yunlong Xu, Yue Song, Yong Li, Di Zhang, Wei Lin, Lin Qu, Bo Zheng
However, we observe that GPU devices in training recommender systems are underutilized, and they cannot attain an expected throughput improvement as what it has achieved in CV and NLP areas.
no code implementations • 8 Apr 2022 • Yong Li, Heng Wang, Xiang Ye
Motivated by ANIL, we rethink the role of adaption in the feature extractor of CNAPs, which is a state-of-the-art representative few-shot method.
1 code implementation • 26 Feb 2022 • Yu Zheng, Chen Gao, Jianxin Chang, Yanan Niu, Yang song, Depeng Jin, Yong Li
Modeling user's long-term and short-term interests is crucial for accurate recommendation.
no code implementations • 17 Jan 2022 • Liang Chen, Qibiao Peng, Jintang Li, Yang Liu, Jiawei Chen, Yong Li, Zibin Zheng
To address such a challenge, we set the trigger as a single node, and the backdoor is activated when the trigger node is connected to the target node.
no code implementations • 14 Jan 2022 • Baole Ai, Zhou Qin, Wenting Shen, Yong Li
Graph Neural Networks (GNNs) have shown promising results in various tasks, among which link prediction is an important one.
no code implementations • 15 Dec 2021 • Huiming Chen, Huandong Wang, Quanming Yao, Yong Li, Depeng Jin, Qiang Yang
Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning.
no code implementations • NeurIPS 2021 • Chen Gao, Yinfeng Li, Quanming Yao, Depeng Jin, Yong Li
Deep sparse networks (DSNs), of which the crux is exploring the high-order feature interactions, have become the state-of-the-art on the prediction task with high-sparsity features.
1 code implementation • 5 Nov 2021 • Zirui Zhu, Chen Gao, Xu Chen, Nian Li, Depeng Jin, Yong Li
With the hypergraph convolutional networks, the social relations can be modeled in a more fine-grained manner, which more accurately depicts real users' preferences, and benefits the recommendation performance.
no code implementations • 1 Nov 2021 • Huandong Wang, Qiaohong Yu, Yu Liu, Depeng Jin, Yong Li
Further, a complex embedding model with elaborately designed scoring functions is proposed to measure the plausibility of facts in STKG to solve the knowledge graph completion problem, which considers temporal dynamics of the mobility patterns and utilizes PoI categories as the auxiliary information and background knowledge.
no code implementations • 1 Nov 2021 • Chang Liu, Chen Gao, Depeng Jin, Yong Li
We first conduct information propagation on two sub-graphs to learn the representations of POIs and users.
no code implementations • 1 Nov 2021 • Yu Liu, Jingtao Ding, Yong Li
Specifically, motivated by distilled knowledge and rich semantics in KG, we firstly construct an urban KG (UrbanKG) with cities' key elements and semantic relationships captured.
no code implementations • 8 Oct 2021 • Junyang Lin, An Yang, Jinze Bai, Chang Zhou, Le Jiang, Xianyan Jia, Ang Wang, Jie Zhang, Yong Li, Wei Lin, Jingren Zhou, Hongxia Yang
Recent expeditious developments in deep learning algorithms, distributed training, and even hardware design for large models have enabled training extreme-scale models, say GPT-3 and Switch Transformer possessing hundreds of billions or even trillions of parameters.
no code implementations • submitted to TOIS 2021 • Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He, Yong Li
In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems.
1 code implementation • 20 Aug 2021 • Xiawei Guo, Yuhan Quan, Huan Zhao, Quanming Yao, Yong Li, WeiWei Tu
Tabular data prediction (TDP) is one of the most popular industrial applications, and various methods have been designed to improve the prediction performance.
no code implementations • ICCV 2021 • Tao Wang, Yong Li, Jingyang Peng, Yipeng Ma, Xian Wang, Fenglong Song, Youliang Yan
One is a 1D weight vector used for image-level scenario adaptation, the other is a 3D weight map aimed for pixel-wise category fusion.
2 code implementations • 16 Aug 2021 • Yu Zheng, Chen Gao, Liang Chen, Depeng Jin, Yong Li
These years much effort has been devoted to improving the accuracy or relevance of the recommendation system.
1 code implementation • 13 Aug 2021 • Erzhuo Shao, Jie Feng, Yingheng Wang, Tong Xia, Yong Li
Thus, obtaining fine-grained population distribution from coarse-grained distribution becomes an important problem.
no code implementations • 11 Aug 2021 • Yong Li, Yufei Sun, Zhen Cui, Shiguang Shan, Jian Yang
To mitigate racial bias and meantime preserve robust FR, we abstract face identity-related representation as a signal denoising problem and propose a progressive cross transformer (PCT) method for fair face recognition.
no code implementations • 10 Aug 2021 • Zefang Zong, Tao Feng, Tong Xia, Depeng Jin, Yong Li
For each problem, we comprehensively introduce the existing DRL solutions.
1 code implementation • 14 Jul 2021 • Yong Li, Lingjie Lao, Zhen Cui, Shiguang Shan, Jian Yang
To mitigate this issue, we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) in a progressive manner.
no code implementations • 29 Jun 2021 • Xiang Ye, Zihang He, Heng Wang, Yong Li
Instead, we verify the crucial role of feature map multiplication in attention mechanism and uncover a fundamental impact of feature map multiplication on the learned landscapes of CNNs: with the high order non-linearity brought by the feature map multiplication, it played a regularization role on CNNs, which made them learn smoother and more stable landscapes near real samples compared to vanilla CNNs.
1 code implementation • 27 Jun 2021 • Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang song, Depeng Jin, Yong Li
This helps explicitly distinguish users' core interests, by forming dense clusters in the interest graph.
no code implementations • 14 Jun 2021 • Chen Gao, Quanming Yao, Depeng Jin, Yong Li
In this way, we can combinatorially generalize data-specific CF models, which have not been visited in the literature, from SOTA ones.
1 code implementation • CVPR 2021 • Xingkun Xu, Yuge Huang, Pengcheng Shen, Shaoxin Li, Jilin Li, Feiyue Huang, Yong Li, Zhen Cui
Then, an additional penalty term, which is in proportion to the ratio of instance FPR overall FPR, is introduced into the denominator of the softmax-based loss.
no code implementations • 31 May 2021 • An Yang, Junyang Lin, Rui Men, Chang Zhou, Le Jiang, Xianyan Jia, Ang Wang, Jie Zhang, Jiamang Wang, Yong Li, Di Zhang, Wei Lin, Lin Qu, Jingren Zhou, Hongxia Yang
Mixture-of-Experts (MoE) models can achieve promising results with outrageous large amount of parameters but constant computation cost, and thus it has become a trend in model scaling.
no code implementations • 28 May 2021 • Guangyin Jin, Huan Yan, Fuxian Li, Jincai Huang, Yong Li
To address the above problems, a novel graph-based deep learning framework for travel time estimation is proposed in this paper, namely Spatio-Temporal Dual Graph Neural Networks (STDGNN).
no code implementations • 14 May 2021 • Yong Li, Shiguang Shan
The learned sample weights alleviate the negative transfer from two aspects: 1) balance the loss of each task automatically, and 2) suppress the weights of FE samples that have large uncertainties.
1 code implementation • 30 Apr 2021 • Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Depeng Jin, Yong Li
Additionally, there is a severe lack of fair comparison among different methods on the same datasets.
Ranked #2 on Traffic Prediction on NE-BJ
1 code implementation • 20 Apr 2021 • Yu Liu, Quanming Yao, Yong Li
N-ary relational knowledge bases (KBs) represent knowledge with binary and beyond-binary relational facts.
no code implementations • 15 Apr 2021 • Zhenfeng Shao, Yong Li, Xiao Huang, Bowen Cai, Lin Ding, Wenkang Pan, Ya zhang
Ecosystem valuation is a method of assigning a monetary value to an ecosystem with its goods and services, often referred to as ecosystem service value (ESV).
1 code implementation • CVPR 2021 • Hui Lv, Chen Chen, Zhen Cui, Chunyan Xu, Yong Li, Jian Yang
Frame reconstruction (current or future frame) based on Auto-Encoder (AE) is a popular method for video anomaly detection.
no code implementations • 12 Mar 2021 • Chenhao Xu, Jiaqi Ge, Yong Li, Yao Deng, Longxiang Gao, Mengshi Zhang, Yong Xiang, Xi Zheng
Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy.
1 code implementation • CVPR 2021 • Fu-Zhao Ou, Xingyu Chen, Ruixin Zhang, Yuge Huang, Shaoxin Li, Jilin Li, Yong Li, Liujuan Cao, Yuan-Gen Wang
Thus, we propose a novel unsupervised FIQA method that incorporates Similarity Distribution Distance for Face Image Quality Assessment (SDD-FIQA).
no code implementations • 1 Mar 2021 • Junyang Lin, Rui Men, An Yang, Chang Zhou, Ming Ding, Yichang Zhang, Peng Wang, Ang Wang, Le Jiang, Xianyan Jia, Jie Zhang, Jianwei Zhang, Xu Zou, Zhikang Li, Xiaodong Deng, Jie Liu, Jinbao Xue, Huiling Zhou, Jianxin Ma, Jin Yu, Yong Li, Wei Lin, Jingren Zhou, Jie Tang, Hongxia Yang
In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1. 9TB images and 292GB texts that cover a wide range of domains.
no code implementations • 21 Feb 2021 • Zhenyu Han, Fengli Xu, Yong Li, Tao Jiang, Depeng Jin, Jianhua Lu, James A. Evans
With the continued spread of coronavirus, the task of forecasting distinctive COVID-19 growth curves in different cities, which remain inadequately explained by standard epidemiological models, is critical for medical supply and treatment.
1 code implementation • 21 Feb 2021 • Zhenyu Han, Fengli Xu, Jinghan Shi, Yu Shang, Haorui Ma, Pan Hui, Yong Li
To address these challenges, we propose Genetic Meta-Structure Search (GEMS) to automatically optimize meta-structure designs for recommendation on HINs.
no code implementations • 4 Feb 2021 • Tao Feng, Sirui Song, Tong Xia, Yong Li
In this paper, we develop an Individual-based Reinforcement Learning Epidemic Control Agent (IDRLECA) to search for smart epidemic control strategies that can simultaneously minimize infections and the cost of mobility intervention.
1 code implementation • ICLR 2021 • Siyi Liu, Chen Gao, Yihong Chen, Depeng Jin, Yong Li
Existing works that try to address the problem always cause a significant drop in recommendation performance or suffers from the limitation of unaffordable training time cost.
no code implementations • 3 Jan 2021 • Tong Xia, Yunhan Qi, Jie Feng, Fengli Xu, Funing Sun, Diansheng Guo, Yong Li
A considerable amount of mobility data has been accumulated due to the proliferation of location-based service.
no code implementations • 1 Jan 2021 • Hansen Wang, Zefang Zong, Tong Xia, Shuyu Luo, Meng Zheng, Depeng Jin, Yong Li
The large-scale vehicle routing problem is defined based on the classical VRP with usually more than one thousand customers.
no code implementations • 1 Jan 2021 • Hongzhi Shi, Quanming Yao, Yong Li
The score also helps relax the discrete space into a continuous one and can be uniformly transformed into matrix form by the Einstein summation convention.
1 code implementation • 11 Nov 2020 • Ying-Tao Luo, Peng-Qi Li, Dong-Ting Li, Yu-Gui Peng, Zhi-Guo Geng, Shu-Huan Xie, Yong Li, Andrea Alu, Jie Zhu, Xue-Feng Zhu
In quantum mechanics, a norm squared wave function can be interpreted as the probability density that describes the likelihood of a particle to be measured in a given position or momentum.
1 code implementation • NeurIPS 2021 • Fengli Xu, Quanming Yao, Pan Hui, Yong Li
Distinguishing the automorphic equivalence of nodes in a graph plays an essential role in many scientific domains, e. g., computational biologist and social network analysis.
1 code implementation • 3 Nov 2020 • Bochao Wang, Hang Xu, Jiajin Zhang, Chen Chen, Xiaozhi Fang, Yixing Xu, Ning Kang, Lanqing Hong, Chenhan Jiang, Xinyue Cai, Jiawei Li, Fengwei Zhou, Yong Li, Zhicheng Liu, Xinghao Chen, Kai Han, Han Shu, Dehua Song, Yunhe Wang, Wei zhang, Chunjing Xu, Zhenguo Li, Wenzhi Liu, Tong Zhang
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models.
no code implementations • 20 Oct 2020 • Yupeng Jiang, Yong Li, Yipeng Zhou, Xi Zheng
The state-of-the-art privacy-preserving technique in the context of federated learning is user-level differential privacy.
Cryptography and Security Distributed, Parallel, and Cluster Computing
1 code implementation • 14 Oct 2020 • Jun Zhang, Chen Gao, Depeng Jin, Yong Li
Group-buying recommendation for social e-commerce, which recommends an item list when users want to launch a group, plays an important role in the group success ratio and sales.
no code implementations • 12 Oct 2020 • Wenqi Jiang, Zhenhao He, Shuai Zhang, Thomas B. Preußer, Kai Zeng, Liang Feng, Jiansong Zhang, Tongxuan Liu, Yong Li, Jingren Zhou, Ce Zhang, Gustavo Alonso
MicroRec accelerates recommendation inference by (1) redesigning the data structures involved in the embeddings to reduce the number of lookups needed and (2) taking advantage of the availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the latency by enabling parallel lookups.
1 code implementation • NeurIPS 2020 • Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, Depeng Jin
Negative sampling approaches are prevalent in implicit collaborative filtering for obtaining negative labels from massive unlabeled data.
1 code implementation • TNNLS 2020 • Jun Fu, Jing Liu, Jie Jiang, Yong Li, Yongjun Bao, Hanqing Lu
We conduct extensive experiments to validate the effectiveness of our network and achieve new state-of-the-art segmentation performance on four challenging scene segmentation data sets, i. e., Cityscapes, ADE20K, PASCAL Context, and COCO Stuff data sets.
Ranked #8 on Semantic Segmentation on COCO-Stuff test
1 code implementation • 4 Aug 2020 • Sirui Song, Zefang Zong, Yong Li, Xue Liu, Yang Yu
Saving lives or economy is a dilemma for epidemic control in most cities while smart-tracing technology raises people's privacy concerns.
no code implementations • 17 Jul 2020 • Rui Wang, Min Chen, Nadra Guizani, Yong Li, Hamid Gharavi, Kai Hwang
A self-adaptive QoE model is set up that relates the model's accuracy with the computing resources required for training which will allow the experience value of the model to improve.
1 code implementation • 8 Jul 2020 • Yu Liu, Quanming Yao, Yong Li
With the rapid development of knowledge bases (KBs), link prediction task, which completes KBs with missing facts, has been broadly studied in especially binary relational KBs (a. k. a knowledge graph) with powerful tensor decomposition related methods.
no code implementations • 5 Jul 2020 • Yong Li, Andrea Turrini, Xuechao Sun, Lijun Zhang
While this is well-understood in the termination analysis of programs, this is not the case for the language inclusion analysis of B\"uchi automata, where research mainly focused on improving algorithms for proving language inclusion, with the search for counterexamples left to the expensive complementation operation.
3 code implementations • 19 Jun 2020 • Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Depeng Jin, Yong Li
We further demonstrate that the learned embeddings successfully capture the desired causes, and show that DICE guarantees the robustness and interpretability of recommendation.
no code implementations • 18 May 2020 • Yong Li, Moshe Y. Vardi, Lijun Zhang
In this work, we exploit the power of \emph{unambiguity} for the complementation problem of B\"uchi automata by utilizing reduced run directed acyclic graphs (DAGs) over infinite words, in which each vertex has at most one predecessor.
1 code implementation • 7 May 2020 • Jianxin Chang, Chen Gao, Xiangnan He, Yong Li, Depeng Jin
Existing solutions integrate user-item interaction modeling into bundle recommendation by sharing model parameters or learning in a multi-task manner, which cannot explicitly model the affiliation between items and bundles, and fail to explore the decision-making when a user chooses bundles.
no code implementations • 25 Apr 2020 • Mingyang Zhang, Tong Li, Yue Yu, Yong Li, Pan Hui, Yu Zheng
Urban anomalies may result in loss of life or property if not handled properly.
1 code implementation • 2020 IEEE 36th International Conference on Data Engineering (ICDE) 2020 • Hongzhi Shi, Quanming Yao, Qi Guo, Yaguang Li, Lingyu Zhang, Jieping Ye, Yong Li, Yan Liu
Predicting Origin-Destination (OD) flow is a crucial problem for intelligent transportation.
no code implementations • 26 Mar 2020 • Dianlei Xu, Tong Li, Yong Li, Xiang Su, Sasu Tarkoma, Tao Jiang, Jon Crowcroft, Pan Hui
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence.
1 code implementation • 9 Mar 2020 • Yu Zheng, Chen Gao, Xiangnan He, Yong Li, Depeng Jin
Price, an important factor in marketing --- which determines whether a user will make the final purchase decision on an item --- surprisingly, has received relatively little scrutiny.
1 code implementation • 19 Nov 2019 • Suguman Bansal, Yong Li, Lucas M. Tabajara, Moshe Y. Vardi
Our approach utilizes both explicit and symbolic representations of the state-space, and effectively leverages their complementary strengths.
no code implementations • ICCV 2019 • Jun Fu, Jing Liu, Yuhang Wang, Yong Li, Yongjun Bao, Jinhui Tang, Hanqing Lu
Recent works attempt to improve scene parsing performance by exploring different levels of contexts, and typically train a well-designed convolutional network to exploit useful contexts across all pixels equally.
Ranked #72 on Semantic Segmentation on ADE20K val
no code implementations • 3 Nov 2019 • Sirui Song, Tong Xia, Depeng Jin, Pan Hui, Yong Li
In this paper, to reveal urban dynamics, we propose a novel system UrbanRhythm to reveal the urban dynamics hidden in human mobility data.
1 code implementation • 18 Sep 2019 • Xiang Zhang, Lina Yao, Manqing Dong, Zhe Liu, Yu Zhang, Yong Li
Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.
2 code implementations • 28 Jun 2019 • Quanming Yao, Xiangning Chen, James Kwok, Yong Li, Cho-Jui Hsieh
Motivated by the recent success of automated machine learning (AutoML), we propose in this paper the search for simple neural interaction functions (SIF) in CF.
1 code implementation • CVPR 2019 • Yong Li, Jiabei Zeng, Shiguang Shan, Xilin Chen
In this paper, we aim to learn discriminative representation for facial action unit (AU) detection from large amount of videos without manual annotations.
1 code implementation • 28 May 2019 • Yihong Chen, Bei Chen, Xiangnan He, Chen Gao, Yong Li, Jian-Guang Lou, Yue Wang
We show how to employ LambdaOpt on matrix factorization, a classical model that is representative of a large family of recommender models.
1 code implementation • 12 May 2019 • Guanjie Zheng, Yuanhao Xiong, Xinshi Zang, Jie Feng, Hua Wei, Huichu Zhang, Yong Li, Kai Xu, Zhenhui Li
Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions.
no code implementations • 23 Feb 2019 • Rong Zhu, Kun Zhao, Hongxia Yang, Wei. Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou
An increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relationship among potentially billions of elements.
Distributed, Parallel, and Cluster Computing
no code implementations • 1 Jan 2019 • Long Zhang, Xuechao Sun, Yong Li, Zhen-Yu Zhang
Deep neural networks (DNNs) have been widely used in the fields such as natural language processing, computer vision and image recognition.
no code implementations • 25 Oct 2018 • Zefang Zong, Jie Feng, Kechun Liu, Hongzhi Shi, Yong Li
In this paper, we first propose the idea to generate dynamic population distributions in full-time series, then we design dynamic population mapping via deep neural network(DeepDPM), a model that describes both spatial and temporal patterns using coarse data and point of interest information.
no code implementations • 21 Sep 2018 • Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, Lina Yao, Yang song, Depeng Jin
To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task.
12 code implementations • CVPR 2019 • Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, Hanqing Lu
Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively.
Ranked #6 on Semantic Segmentation on Trans10K
no code implementations • 26 Nov 2017 • Donghan Yu, Yong Li, Fengli Xu, Pengyu Zhang, Vassilis Kostakos
In this paper we present the first population-level, city-scale analysis of application usage on smartphones.
no code implementations • 21 Feb 2017 • Fengli Xu, Zhen Tu, Yong Li, Pengyu Zhang, Xiao-Ming Fu, Depeng Jin
By conducting experiments on two real-world datasets collected from both mobile application and cellular network, we reveal that the attack system is able to recover users' trajectories with accuracy about 73%~91% at the scale of tens of thousands to hundreds of thousands users, which indicates severe privacy leakage in such datasets.
Computers and Society Cryptography and Security
1 code implementation • 1 Jun 2016 • Yang Li, Chunxiao Fan, Yong Li, Qiong Wu, Yue Ming
In this paper, we first propose a new activation function, Multiple Parametric Exponential Linear Units (MPELU), aiming to generalize and unify the rectified and exponential linear units.