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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xinke Jiang Rihong Qiu Yongxin Xu Wentao Zhang Yichen Zhu Ruizhe Zhang Yuchen Fang Xu Chu Junfeng Zhao Yasha Wang Key Laboratory of High Confidence Software Technologies (Peking University) School of Computer Science Peking University China University of Electronic Science and Technology of China Key Laboratory of High Confidence Software Technologies (Peking University) School of Computer Science Peking University China and Center on Frontiers of Computing Studies Peking University Beijing China and Peking University Information Technology Institute Tianjin Binhai China Key Laboratory of High Confidence Software Technologies (Peking University) School of Computer Science Peking University China and Big Data Technology Research Center Nanhu Laboratory Jiaxing China Key Laboratory of High Confidence Software Technologies (Peking University) School of Computer Science Peking University China and Peking University Information Technology Institute Tianjin Binhai China

ISBN: (纸本)9798331314385

Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In this paper, we introduce a novel framework called General Retrieval-Augmented Graph Learning (RAGRAPH), which brings external graph data into the general graph foundation model to improve model generalization on unseen scenarios. On the top of our framework is a toy graph vector library that we established, which captures key attributes, such as features and task-specific label information. During inference, the RAGRAPH adeptly retrieves similar toy graphs based on key similarities in downstream tasks, integrating the retrieved data to enrich the learning context via the message-passing prompting mechanism. Our extensive experimental evaluations demonstrate that RAGRAPH significantly outperforms state-of-the-art graph learning methods in multiple tasks such as node classification, link prediction, and graph classification across both dynamic and static datasets. Furthermore, extensive testing confirms that RAGRAPH consistently maintains high performance without the need for task-specific fine-tuning, highlighting its adaptability, robustness, and broad applicability.

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Spatiotemporal water quality data reconstruction: A tensor factorization framework

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Ecological Informatics 2025年

作者： Xuke Wu Kun Shan Lan Wang Jingkai Wang Mingsheng Shang School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing 400714 China University of Chinese Academy of Sciences Beijing 100049 China

Automatic high-frequency monitoring (AHFM) of water quality parameters has gained growing attention for managing eutrophic lakes. However, missing data in water quality datasets remains a persistent challenge, often compromising the reliability of mathematical models and statistical analyses. While traditional imputation methods fail to adequately capture complex spatiotemporal dependencies among water quality variables, this study proposes a novel nonnegative tensor factorization (NTF) model designed to reconstruct missing values by effectively modeling variable-site-time triad interactions. Previous findings indicate that incorporating bias schemes into NTF architectures substantially reduces underfitting risks. Leveraging this insight, we develop and rigorously evaluate seven distinct biased NTF variants. Their diversified bias term designs not only enhance individual model performance but also enable highly effective ensemble learning through complementary strengths. To validate the proposed models, we conduct comprehensive experiments using real-world AHFM data from Lake Dianchi, China, under various missing data scenarios (20–80 % missing ratios and 1–4 weeks missing gaps). The key water quality parameters include chlorophyll- a concentration, water temperature, pH, dissolved oxygen, electrical conductivity, turbidity, chemical oxygen demand, ammonia, total phosphorus, and total nitrogen. The results demonstrate the superiority of the seven biased NTF models, achieving optimal performance with a root mean squared error (RMSE) of 0.2796 ± 0.0041, mean absolute error (MAE) of 0.1611 ± 0.0034, and Nash-Sutcliffe efficiency (NSE) of 0.9704 ± 0.0009 across all missingness scenarios. Compared to state-of-the-art models, these methods yield consistent improvements of 3.42 %–30.74 % in RMSE, 2.30 %–30.38 % in MAE, and 0.20 %–3.22 % in NSE. Notably, an ensemble of the seven models further elevates imputation accuracy, attaining an RMSE of 0.2409 ± 0.0018, MAE of 0.1384

关键词： Water quality Missing data imputation Nonnegative tensor factorization Ensemble Bias scheme Lake Dianchi

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Medical-VLBERT: Medical visual language BERT for COVID-19 CT report generation with alternate learning

arXiv

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arXiv 2021年

作者： Liu, Guangyi Liao, Yinghong Wang, Fuyu Zhang, Bin Zhang, Lu Liang, Xiaodan Wan, Xiang Li, Shaolin Li, Zhen Zhang, Shuixing Cui, Shuguang The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Sun Yat-sen University Guangzhou510275 China Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou510630 China Jinan University Guangzhou510632 China Fifth Affiliated Hospital of Sun Yat-sen University Zhuhai519000 China

Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further finetuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at at https://***/. Copyright © 2021, The Authors. All rights reserved.

关键词： Diagnosis

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Hyper-Parameter-Free Latent Factor Analysis via Adaptive Moment Estimation-Incorporated Particle Swarm Optimization

Hyper-Parameter-Free Latent Factor Analysis via Adaptive Mom...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jiufang Chen Ye Yuan Chunlin He School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chinese Academy of Sciences Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing School University of Chinese Academy of Sciences Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing China School of Computer Science China West Normal University Nanchong Sichuan China

ISBN: (纸本)9781665426220

A latent factor analysis (LFA) model can efficiently address a high-dimensional and sparse (HiDS) matrix with a stochastic gradient descent (SGD) algorithm. However, an SGD-based LFA model's performance depends heavily on its hyper-parameters. The popular method is based on grid-search, which fails because of expensive computation and time-consuming. Aiming at implementing a hyper-parameter-free LFA model, this study proposes an adaptive moment estimation-incorporated particle swarm optimization (Adam-PSO) algorithm that efficiently addresses the premature issues in a PSO algorithm. With achieved the hyper-parameter adaptation in an SGD-based LFA model, an Adam-PSO-based LFA (APL) model possessing hyper-parameter-free training is further implemented. Empirical studies on four HiDS matrices indicate that compared with state-of-the-art models with hyper-parameter adaptation settings, an APL model achieves the most efficient hyper-parameter-free training and highly competitive prediction accuracy for missing data of an HiDS matrix. Hence, it fits the need of real applications with high scalability and efficiency.

关键词： Training Adaptation models Computational modeling Stochastic processes Predictive models Prediction algorithms data models

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Deep Sentiment Learning Network for Temporal-aware Recommendation Based on User Reviews

Deep Sentiment Learning Network for Temporal-aware Recommend...

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IEEE International Conference on big data Analysis (ICBDA)

作者： Xinxin Li Tianqi Shang Dezhong Peng Xiaoyu Shi School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Computer Science Sichuan University Chengdu China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

Recently, many websites encourage users to fill in reviews on items and services to improve the quality of personalized recommendation, because of the rich sentiment information hidden in user reviews. However, most existing recommendation methods based on reviews heavily emphasize extracting the rich sentiment information from reviews by using deep learning technologies as far as possible and always ignore the effects of temporal dynamical of user preferences. To address the above issues, we propose DSL- TR, a deep sentiment learning framework for the temporal-aware recommendation, using an intricate combination of bidirectional long short-term memory (BLSTM) and Convolution Neural Network (CNN). It consists of four layers: embedding layer, BLSTM layer, CNN layer and feature fusion layer. In the embedding layer, we explicitly combine the time information in the review embedding layer by regarding the review time as an independent factor. Then, we utilize the BLSTM and CNN networks to extract the long and short-terms of latent features of user and item in parallel. Finally, the factorization machine technique, as a rating predictor, is introduced on the last layer to estimate the rating based on the learned user and item latent features. We conduct extensive experiments on Amazon's four data sets. The results demonstrate that our proposed method outperforms several state-of-art methods consistently.

关键词： Deep learning Convolution Conferences Neural networks big data Feature extraction data mining

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Improved Weak Lensing Photometric Redshift Calibration via StratLearn and Hierarchical Modeling

arXiv

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arXiv 2024年

作者： Autenrieth, Maximilian Wright, Angus H. Trotta, Roberto van Dyk, David A. Stenning, David C. Joachimi, Benjamin Department of Mathematics Imperial College London 180 Queen’s Gate LondonSW7 2AZ United Kingdom German Centre for Cosmological Lensing Bochum44780 Germany SISSA International School for Advanced Studies Via Bonomea 265 Trieste34136 Italy Department of Physics Imperial College London Blackett Laboratory Prince Consort Rd LondonSW72AZ United Kingdom Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy INFN – National Institute for Nuclear Physics Via Valerio 2 Trieste34127 Italy Department of Statistics and Actuarial Science Simon Fraser University Canada Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom

Discrepancies between cosmological parameter estimates from cosmic shear surveys and from recent Planck cosmic microwave background measurements challenge the ability of the highly successful ΛCDM model to describe the nature of the Universe. To rule out systematic biases in cosmic shear survey analyses, accurate redshift calibration within tomographic bins is key. In this paper, we improve photo-z calibration via Bayesian hierarchical modeling of full galaxy photo-z conditional densities, by employing StratLearn, a recently developed statistical methodology, which accounts for systematic differences in the distribution of the spectroscopic training/source set and the photometric target set. Using realistic simulations that were designed to resemble the KiDS+VIKING-450 dataset, we show that StratLearn-estimated conditional densities improve the galaxy tomographic bin assignment, and that our StratLearn-Bayesian framework leads to nearly unbiased estimates of the target population means. This leads to a factor of ∼ 2 improvement upon the previously best photo-z calibration method. Our approach delivers a maximum bias per tomographic bin of ∆〈z〉 = 0.0095 ± 0.0089, with an average absolute bias of 0.0052 ± 0.0067 across the five tomographic bins. © 2024, CC BY.

关键词： Galaxies

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Quantum Walks–Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration

arXiv

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arXiv 2025年

作者： Chang, Yen Jui Wang, Wei-Ting Liu, Chen-Yu Wang, Yun-Yuan Chang, Ching-Ray Master Program in Intelligent Computing and Big Data Chung Yuan Christian University No. 200 Zhongbei Rd. Zhongli Dist Taoyuan City320314 Taiwan Quantum Information Center Chung Yuan Christian University No. 200 Zhongbei Rd. Zhongli Dist Taoyuan City320314 Taiwan Department of Physics National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan Graduate Institute of Applied Physics National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan NVIDIA AI Technology Center NVIDIA Corp Taipei610101 Taiwan

We present a novel Adaptive Distribution Generator (ADG) that leverages a quantum walks–based approach to generate high precision and efficiency of target probability distributions. Our method integrates variational quantum circuits with discrete-time quantum walks(DTQWs)—specifically, split-step quantum walks(SSQWs) and their entangled extensions—to dynamically tune coin parameters and drive the evolution of quantum states towards desired distributions. This enables accurate one-dimensional probability modeling for applications such as financial simulation and structured two-dimensional pattern generation exemplified by digit representations (0–9). Implemented within the CUDA-Q framework, our approach exploits GPU acceleration to significantly reduce computational overhead and improve scalability relative to conventional methods. Extensive benchmarks demonstrate that our Quantum Walk–Based Adaptive Distribution Generator (QWs-based ADG) achieves high simulation fidelity and bridges the gap between theoretical quantum algorithms and practical high-performance computation. © 2025, CC BY.

关键词： Qubits

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Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks

Graph Regularized Nonnegative Latent Factor Analysis Model f...

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IEEE International Conference on Networking, Sensing and Control

作者： Yue Zhou ZhiGang Liu Ye Yuan School of Computer Science and Technology Choncgqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing 400714 China and with the Chongqing School University of Chinese Academy of Sciences Chongqing China College of Computer and Information Science Southwest University Chongqing China

ISBN: (纸本)9781665472449

With the development of blockchain technology, a cryptocurrency based on blockchain technology is becoming more and more popular. The huge cryptocurrency transaction network has therefore received widespread attention. The link prediction learning structure of the network is supportive to understand the mechanism of networks, so it also has been widely studied in the cryptocurrency network. However, the dynamics of cryptocurrency transaction networks have been neglected in past studies. In this study, therefore, we use a graph-regularized method to link past transaction records with future transactions. Based on this, we propose a single latent factor-dependent, nonnegative, multiplicative, and graph regularized-incorporated update (SLF-NMGRU) algorithm and further propose a graph regularized nonnegative latent factor analysis (GrNLFA) model. Eventually, the experimental results on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and computational efficiency.

关键词： Analytical models Tensors Computational modeling Heuristic algorithms Optimization methods Predictive models Prediction algorithms

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FlickerNet: Adaptive 3D gesture recognition from sparse point clouds 30

FlickerNet: Adaptive 3D gesture recognition from sparse poin...

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30th British Machine Vision Conference, BMVC 2019

作者： Min, Yuecong Chai, Xiujuan Zhao, Lei Chen, Xilin Inst. of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Agricultural Information Institute Chinese Academy of Agricultural Sciences Key Lab of Agricultural Big Data Ministry of Agriculture Beijing100081 China HUAWEI Technologies CO. LTD. Beijing100095 China

Recent studies on gesture recognition use deep convolutional neural networks (CNNs) to extract spatio-temporal features from individual frames or short video clips. However, extracting features frame-by-frame will bring a lot of redundant and ambiguous gesture information. Inspired by the flicker fusion phenomena, we propose a simple but efficient network, called FlickerNet, to recognize gesture from a sequence of sparse point clouds sampled from depth videos. Different from the existing CNN-based methods, FlickerNet can adaptively recognize hand postures and hand motions from the flicker of gestures: the point clouds of the stable hand postures and the sparse point-cloud motion for fast hand motions. Notably, FlickerNet significantly outperforms the previous state-of-the-art approaches on two challenging datasets with much higher computational efficiency. © 2019. The copyright of this document resides with its authors.

关键词： Computational efficiency

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UEIN: A User Evolving Interests Network for Click-Through Rate Prediction

UEIN: A User Evolving Interests Network for Click-Through Ra...

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big data Science and Engineering (bigdataSE), IEEE International Conference on

作者： Jianxiong Xu Xiaoyu Shi Hezhe Qiao Mingsheng Shang Xianbo He Qingyu He The Computer School of China West Normal University Nanchong Sichuan China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China College of Land and Resources China West Normal University Nanchong Sichuan China

ISBN: (纸本)9781665400398

The Click-Through Rate (CTR) prediction via modeling the change of user interest is of great significance task to industrial applications. Most of the existing methods based on collaborative filtering (CF) and recurrent neural network (RNN) that focus on capturing user's long-term and short-term interests separately, or combine these interests in a tough way. However, the effect of long- and short-term interests accounts different ratios to the next-item decision of user, rather than equally. To address this issue, we propose UEIN, a novel User Evolving Interest Network for CTR prediction task, that can capture the change of user preference via adaptively fusion the user's long-term and short-term interests over time. To explicitly learn the user preference on item category, we add the item category information into user behavior sequence, by clustering the items into different categories. Then we divide user historical behaviors into two sequence parts, long-term and short-term behaviors sequence. Based on this, we leverage the attention network to learn the user's current interest from the short-term behaviors sequence, while a dense network is adopted to extract the user's general interest from the long-term sequence. After that, an adaptive fusion mechanism is proposed to combine the current and general interest adaptively, and the prediction result is generated based on the user's combined interest. Extensive experimental results on industrial datasets demonstrated that the superiority of our proposed model, compared to the state-of-the-art models.

关键词： Adaptation models Recurrent neural networks Conferences Collaborative filtering Clustering methods Predictive models Feature extraction

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