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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Li, Youhua Du, Hanwen Ni, Yongxin Zhao, Pengpeng Guo, Qi Yuan, Fajie Zhou, Xiaofang ShanghaiTech University Shanghai China Shanghai Innovation Center for Processor Technologies SHIC China Westlake University Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Institute of Computing Technology State Key Lab of Processors Chinese Academy of Sciences Beijing China The Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9798350317152

ID-based Recommender Systems (RecSys), where each item is assigned a unique identifier and subsequently converted into an embedding vector, have dominated the de-signing of RecSys. Though prevalent, such ID-based paradigm is not suitable for developing transferable RecSys and is also susceptible to the cold -start issue. In this paper, we unleash the boundaries of the ID-based paradigm and propose a Pure Multi-Modality based Recommender system (PMMRec), which relies solely on the multi-modal contents of the items (e.g., texts and images) and learns transition patterns general enough to transfer across domains and platforms. Specifically, we design a plug-and-play framework architecture consisting of multi-modal item encoders, a fusion module, and a user encoder. To align the cross-modal item representations, we propose a novel next-item enhanced cross-modal contrastive learning objective, which is equipped with both inter-and intra-modality negative samples and explicitly incorporates the transition patterns of user behaviors into the item encoders. To ensure the robustness of user representations, we propose a novel noised item detection objective and a robustness-aware contrastive learning objective, which work together to denoise user sequences in a self-supervised manner. PMMRec is designed to be loosely coupled, so after being pre-trained on the source data, each component can be transferred alone, or in conjunction with other components, allowing PMMRec to achieve versatility under both multi-modality and single-modality transfer learning settings. Extensive experiments on 4 sources and 10 target datasets demonstrate that PMMRec surpasses the state-of-the-art recommenders in both recommendation performance and transferability. Our code and dataset is available at: https://***/ICDE24IPMMRec. © 2024 IEEE.

关键词： Recommender systems

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Provably efficient exploration in quantum reinforcement learning with logarithmic worst-case regret 24

Provably efficient exploration in quantum reinforcement lear...

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Proceedings of the 41st International Conference on Machine Learning

作者： Han Zhong Jiachen Hu Yecheng Xue Tongyang Li Liwei Wang Center for Data Science Peking University School of Computer Science Peking University School of Computer Science and Center on Frontiers of Computing Studies Peking University Center for Data Science and 4National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University

While quantum reinforcement learning (RL) has attracted a surge of attention recently, its theoretical understanding is limited. In particular, it remains elusive how to design provably efficient quantum RL algorithms that can address the exploration-exploitation trade-off. To this end, we propose a novel UCRL-style algorithm that takes advantage of quantum computing for tabular Markov decision processes (MDPs) with S states, A actions, and horizon H, and establish an O(poly(S, A, H, log T)) worst-case regret for it, where T is the number of episodes. Furthermore, we extend our results to quantum RL with linear function approximation, which is capable of handling problems with large state spaces. Specifically, we develop a quantum algorithm based on value target regression (VTR) for linear mixture MDPs with d-dimensional linear representation and prove that it enjoys O(poly(d, H, log T)) regret. Our algorithms are variants of UCRL/UCRLVTR algorithms in classical RL, which also leverage a novel combination of lazy updating mechanisms and quantum estimation subroutines. This is the key to breaking the Ω(√T)-regret barrier in classical RL. To the best of our knowledge, this is the first work studying the online exploration in quantum RL with provable logarithmic worst-case regret.

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DeepPointMap2: Accurate and Robust LiDAR-Visual SLAM with Neural Descriptors 24

DeepPointMap2: Accurate and Robust LiDAR-Visual SLAM with Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Xiaze Ding, Ziheng Jing, Qi Cheng, Ying Ding, Wenchao Feng, Rui Fudan University Shanghai China The School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China The Academy for Engineering and Technology Fudan University China The Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

ISBN: (纸本)9798400706868

Simultaneous Localization and Mapping (SLAM) plays a pivotal role in autonomous driving and robotics. Existing methods often rely on hand-craft feature extraction and cross-modal fusion techniques, resulting in limited feature representation capability and reduced robustness. To address this challenge, we introduce DeepPointMap2, a novel learning-based LiDAR-Visual SLAM architecture that leverages neural descriptors to tackle multiple SLAM sub-tasks in a unified manner. Our approach employs neural networks to extract multi-modal tokens, which are then adaptively fused by the Visual-Point Fusion Module to generate sparse 3D neural descriptors, ensuring precise and robust performance. As a pioneering work, our method achieves state-of-the-art localization performance among various Visual-, LiDAR-, and Visual-LiDAR-based methods in widely-used benchmarks, as shown in the experiment results. Furthermore, the approach proves to be robust in scenarios involving camera failure and LiDAR obstruction. © 2024 ACM.

关键词： SLAM robotics

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EmoDamp: A Model for Speaker Emotion Inference in Dialogues with Bidirectional Emotion Damping Mechanism 6

EmoDamp: A Model for Speaker Emotion Inference in Dialogues ...

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6th International Conference on Algorithms, computing and Artificial Intelligence, ACAI 2023

作者： Jiang, Yueqi Sun, Xiao Wang, Jiamin AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei China

ISBN: (纸本)9798400709203

Emotion analysis in conversation has been a popular research topic in the natural language processing field. While much of the existing research has focused on emotion recognition in conversation, the emotion inference task in conversation is more challenging due to its specific prerequisites. In this paper, we propose EmoDamp, a novel model for the conversational emotion inference task. By referring to psychological theories, a bidirectional emotion damping network(BED Net) is designed to model the emotion generation mechanism. Furthermore, external commonsense knowledge is also used to supplement the prediction results to make them more consistent with authentic logic. Finally, a conditional random field (CRF) is used to capture the potential constraints of the emotion transfer process in conversation and simulate emotion contagion. Experimental results on two benchmark datasets demonstrate that EmoDamp outperforms the baseline model, achieving improved results on each emotion category and showcasing its effectiveness. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Human computer interaction

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iSTIMULI: Prescriptive Stimulus Design for Eye Movement Analysis of Patients with Parkinson’s Disease 16th

iSTIMULI: Prescriptive Stimulus Design for Eye Movement Ana...

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16th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2023

作者： Akshay, S. Amudha, J. Kulkarni, Nilima Prashanth, L.K. Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Karnataka Mysuru India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Karnataka Bengaluru India Department of Computer Science and Engineering MIT SOC MIT Art Design and Technology University Pune India Parkinson’s and Movement Disorders Clinic Karnataka Bengaluru India Center for Parkinson’s Disease and Movement Disorders Manipal Hospital Miller’s road Bangalore India

ISBN: (纸本)9783031364013

With an increase in eye-tracking applications, there is a need for eye gaze data analytics to widen its scope to provide customized solutions. However, among several types of analytics such as predictive, descriptive, diagnostic, and prescriptive analytics providing insight into it is quite challenging. This work presents the need and role of prescriptive data analysis that can be carried out on eye gaze data pertaining to a specific study of research on the diagnosis of Parkinson’s disease patients during visual search and visual attention tasks. We further look at the various aspects like the image stimulus used, a task performed by the viewer, and its relation to the viewer’s eye movement behavior. iSTIMULI includes the design of the image stimuli used to classify the eye movements of Parkinson’s disease patients from healthy controls. Visualizations of eye movements on the image stimulus designed are presented. A machine learning analysis is also provided that substantiates the significance of the stimuli with the highest f-measure of 73%. iSTIMULI establishes a relation between image stimulus and viewer behavior. As a prescriptive analysis model, iSTIMULI calculates the risk based on eye movements and recommends actions to be taken in the future to improve the ability of the participant. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Eye movements

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Research on Fault Diagnosis Method Based on Structural Causal Model in Tennessee Eastman Process 7th

Research on Fault Diagnosis Method Based on Structural Causa...

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7th International Conference on computing, Control and Industrial Engineering, CCIE 2023

作者： Pu, Haoyuan Liu, Jie Chen, Zhi Yang, Xiaohua Ren, Changan Xu, Zhuoran Jian, Yifan School of Computer Science University of South China Hengyang China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hengyang China School of Nuclear Science and Technology University of South China Hengyang China

ISBN: (纸本)9789819927296

In the practical application of fault diagnosis in large-scale chemical systems, due to the danger of faults, the machine learning diagnosis methods used in current research often face the problem of scarcity of fault samples. This makes it difficult for the model to be trained effectively, thus affecting the fault detection rate of the model. Therefore, for the fault diagnosis of chemical system with small sample data, this paper proposes a diagnostic method that uses structural causal model to further combine the diagnostic results of the two machine learning methods, thereby improving global diagnostic performance. With the help of powerful system description ability of structural causal model, we can establish a causal graph for diagnostic process of the chemical system, and accurately construct the corresponding structural equations for the various diagnostic results of the two machine learning methods to combine them, thus improve the fault detection rate. The global results make up for the shortcomings of single machine learning method in small sample chemical system fault diagnosis. The verification on the chemical system simulation platform Tennessee Eastman process shows that, compared with the diagnosis results of the selected Gaussian Naive Bayes method and the K-nearest neighbour method, the results obtained by our method are effectively improved in fault detection rate of each fault in TEP. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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Steel surface defect detection based on lightweight model 38

Steel surface defect detection based on lightweight model

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： He, Le Huang, Jun Li, Xue Guan, Bolun Anhui University of Technology School of Computer Science and Computing Ma'Anshan China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China Agricultural Economy and Information Research Institute Anhui Academy of Agricultural Sciences Hefei China

ISBN: (纸本)9798350303636

Steel surface defect detection, with the goal of enhancing the quality of steel within the industry, is a vital process in steel production. Over the years, a number of techniques have been developed to meet the challenge of object detection. Nonetheless, designing lightweight models that can detect defects quickly and accurately remains challenging due to a significant amount of environmental interference and limited computational resources of the edge devices used in steel factories. In this paper, In this paper, we propose an improved Lightweight Detection Network(LDN) for steel surface defect detection. First, to extract features, the lightweight MobileNetV2 model is employed as the backbone. Second, to improve the detection accuracy, a feedback mechanism from the feature pyramid is added to the backbone network to integrate the features from shallow and deep layers. Additionally, the Efficient Intersection over Union(EIoU) loss is utilized to make the regression more accurate. The experimental results indicate that the method's accuracy in recognition surpasses that of previous models, and the parameters and computational requirements have decreased significantly, leading to a rapid and precise detection of steel surface defects. © 2023 IEEE.

关键词： Defect Detection Feature Pyramid Lightweight Detection Network

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Multi-Channel Graph Fusion Representation for Tabular Data Imputation

Multi-Channel Graph Fusion Representation for Tabular Data I...

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International Joint Conference on Neural Networks (IJCNN)

作者： Na Yu Ke Xu Kaixuan Chen Shunyu Liu Tongya Zheng Mingli Song College of Computer Science and Technology Zhejiang University Hangzhou China Big Graph Center School of Computer and Computing Science Hangzhou City University Hangzhou China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The unprecedented success of deep learning has revolutionized the imputation mechanism of missing values in tabular data, typically caused by data corruption and sensor noise. One promising approach involves mining the latent relationship among all entities of tabular data to complete the missing values from similar entities. However, the various data absence challenges an effective identification of similar entities with missing attributes. Moreover, this limited entity-level relationship fails to capture the intricate interdependencies that typically arise between different attributes, leading to potential bias in data imputation. In this paper, we propose to build an innovative customized graph for tabular data, termed MCG4Table, that allows us to facilitate the representation of intra-entity, intra-attribute, and entity-attribute relationships. At the heart of our approach is a novel strategy to establish a multi-channel graph by dividing diverse relationships concealed within the tabular data from different perspectives. Moreover, MCG4Table introduces a two-stage channel feature fusion architecture, namely a homogeneous-then-heterogeneous fusion strategy, yielding enhanced representations of entities and attributes for the downstream imputation task. Extensive experiments on several benchmark datasets demonstrate the superiority of our proposed method. Elaborate ablation studies and parameter sensitivity analysis verify the effectiveness and robustness of our dedicated strategies. Our code will be made publicly available.

关键词： Heart Deep learning Sensitivity analysis Fuses Noise Neural networks Benchmark testing

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NAGPHORMER: A TOKENIZED GRAPH TRANSFORMER FOR NODE CLASSIFICATION IN LARGE GRAPHS

arXiv

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arXiv 2022年

作者： Chen, Jinsong Gao, Kaiyuan Li, Gaichao He, Kun Institute of Artificial Intelligence Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China Hopcroft Center on Computing Science Huazhong University of Science and Technology China

The graph Transformer emerges as a new architecture and has shown superior performance on various graph mining tasks. In this work, we observe that existing graph Transformers treat nodes as independent tokens and construct a single long sequence composed of all node tokens so as to train the Transformer model, causing it hard to scale to large graphs due to the quadratic complexity on the number of nodes for the self-attention computation. To this end, we propose a Neighborhood Aggregation Graph Transformer (NAGphormer) that treats each node as a sequence containing a series of tokens constructed by our proposed Hop2Token module. For each node, Hop2Token aggregates the neighborhood features from different hops into different representations and thereby produces a sequence of token vectors as one input. In this way, NAGphormer could be trained in a mini-batch manner and thus could scale to large graphs. Moreover, we mathematically show that as compared to a category of advanced Graph Neural Networks (GNNs), the decoupled Graph Convolutional Network, NAGphormer could learn more informative node representations from the multi-hop neighborhoods. Extensive experiments on benchmark datasets from small to large are conducted to demonstrate that NAGphormer consistently outperforms existing graph Transformers and mainstream GNNs. Code is available at https://***/JHL-HUST/NAGphormer. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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A Graph-Based Method for AI Chip Operator Optimization Using Deep Learning

A Graph-Based Method for AI Chip Operator Optimization Using...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Jiapeng Yan Qilong Zheng School of Computer Science University of Science and Technology of China (USTC) Hefei China National High Performance Computing Center University of Science and Technology of China (USTC) Hefei China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

AI operators refer to reusable code programs encapsulated in AI chip frameworks that implement specific functions. To achieve high performance, it is necessary to combine hardware characteristics efficiently when programming. However, for operators with multiple computational steps, it is challenging to develop excellent scheduling strategies. To address this issue, this paper proposes a graph-based method for AI chip operator optimization. Firstly, establish bidirectional transformation relationship between operators and corresponding computation graphs. Then, use deepwalk and word2vec to convert operators’ computation graphs into embedding representations, and optimize corresponding operators by annotating the nodes using graph neural network. Also, simple operator fusion can be achieved by fusing graphs of multiple operators and optimizing the fused operator. Through the creation of an operator dataset and related experiments within the Cambricon community framework, this method demonstrates superior optimization and fusion of element-wise operators compared to other simple tuning methods.

关键词： Deep learning Training Codes Computational modeling AI accelerators Programming Rendering (computer graphics)

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