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41st International Conference on Machine Learning, ICML 2024

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright 2024 by the author(s)

关键词： Graph theory

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Variational Encoder Based Synthetic Alzheimer's data Generation for Deep Learning, XGBoost and Statistical Survival Analysis

Variational Encoder Based Synthetic Alzheimer's Data Generat...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Henry Musto Daniel Stamate Daniel Stahl Department of Computing Goldsmiths Data Science and Soft Computing Lab University of London London United Kingdom Data Science and Soft Computing Lab Department of Computing Goldsmiths Univerisity of London and School of Health Sciences The University of Manchester United Kingdom Department of Biostatistics Institute of Psychiatry Psychology and Neuroscience Kings College London London United Kingdom

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Alzheimer's Disease (AD) poses significant challenges in research due to limited access to longitudinal patient data caused by privacy constraints. This study uses deep learning, specifically Variational Autoencoders (VAEs), to generate synthetic datasets that replicate real-world AD data while preserving privacy. These datasets were used to train and compare survival analysis models, including Survival XGBoost, Survival Transformers, and the Cox Proportional Hazards model. Results show that deep learning and boosting models outperform the standard Cox model in predicting the progression of AD, achieving a higher degree of accuracy. Our findings suggest that combining synthetic data with advanced machine learning models can improve predictive capabilities in healthcare research, particularly when real patient data is restricted.

关键词： Deep learning data privacy Analytical models Accuracy Computational modeling Medical services Predictive models data models Alzheimer's disease Synthetic data

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Exploring the Role of Diversity in Example Selection for In-Context Learning

arXiv

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arXiv 2025年

作者： Kapuriya, Janak Kaushik, Manit Ganguly, Debasis Bhatia, Sumit Data Science Institute University of Galway Galway Ireland Computer Science and Engineering IIIT Delhi New Delhi India School of Computing University of Glasgow Glasgow United Kingdom Media and Data Science Research Lab Adobe Systems Noida India

In-Context Learning (ICL) has gained prominence due to its ability to perform tasks without requiring extensive training data and its robustness to noisy labels. A typical ICL workflow involves selecting localized examples relevant to a given input using sparse or dense embedding-based similarity functions. However, relying solely on similarity-based selection may introduce topical biases in the retrieved contexts, potentially leading to suboptimal downstream performance. We posit that reranking the retrieved context to enhance topical diversity can improve downstream task performance. To achieve this, we leverage maximum marginal relevance (MMR) which balances topical similarity with inter-example diversity. Our experimental results demonstrate that diversifying the selected examples leads to consistent improvements in downstream performance across various context sizes and similarity functions. The implementation of our approach is made available at https://***/janak11111/Diverse-ICL. © 2025, CC BY.

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Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced data: Improving Machine Failure Prediction

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Computers, Materials & Continua 2023年第6期75卷 4821-4841页

作者： Yap Bee Wah Azlan Ismail Nur Niswah Naslina Azid Jafreezal Jaafar Izzatdin Abdul Aziz Mohd Hilmi Hasan Jasni Mohamad Zain Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA(UiTM)Shah Alam40450SelangorMalaysia School of Computing Sciences College of ComputingInformatics and MediaUniversiti Teknologi MARA(UiTM)40450Shah AlamSelangorMalaysia Mathematical Sciences Studies College of ComputingInformatics and MediaUniversiti Teknologi MARA(UiTM)Kelantan BranchMachang CampusBukit Ilmu18500MachangKelantan Darul NaimMalaysia Centre for Research in Data Science(CeRDaS) Department of Computer and Information Sciences(DCIS)Universiti Teknologi PETRONAS(UTP)Seri Iskandar32610PerakMalaysia UNITAR International University Jalan SS6/3SS6Petaling Jaya47301SelangorMalaysia

Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure *** common approach to han-dle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling,random oversampling,or Synthetic Minority Oversampling Technique(SMOTE)*** paper compared the classification performance of three popular classifiers(Logistic Regression,Gaussian Naïve Bayes,and Support Vector Machine)in predicting machine failure in the Oil and Gas *** original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945(97%)‘non-failure’and 528(3%)‘failure data’.The three independent variables to predict machine failure were pressure indicator,flow indicator,and level *** accuracy of the classifiers is very high and close to 100%,but the sensitivity of all classifiers using the original dataset was close to *** performance of the three classifiers was then evaluated for data with different imbalance rates(10%to 50%)generated from the original data using SMOTE,SMOTE-Support Vector Machine(SMOTE-SVM)and SMOTE-Edited Nearest Neighbour(SMOTE-ENN).The classifiers were evaluated based on improvement in sensitivity and *** showed that the sensitivity of all classifiers increases as the imbalance rate *** with radial basis function(RBF)kernel has the highest sensitivity when data is balanced(50:50)using SMOTE(Sensitivitytest=0.5686,Ftest=0.6927)compared to Naïve Bayes(Sensitivitytest=0.4033,Ftest=0.6218)and Logistic Regression(Sensitivitytest=0.4194,Ftest=0.621).Overall,the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases,but the sensitivity is below 50%.The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN.

关键词： Machine failure machine learning imbalanced data SMOTE classification

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MVMoE: multi-task vehicle routing solver with mixture-of-experts 24

MVMoE: multi-task vehicle routing solver with mixture-of-exp...

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Proceedings of the 41st International Conference on Machine Learning

作者： Jianan Zhou Zhiguang Cao Yaoxin Wu Wen Song Yining Ma Jie Zhang Chi Xu College of Computing and Data Science Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Department of Information Systems Eindhoven University of Technology The Netherlands Institute of Marine Science and Technology Shandong University China College of Computing and Data Science Nanyang Technological University Singapore and Singapore Institute of Manufacturing Technology (SIMTech) Agency for Science Technology and Research (A*STAR) Singapore

Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants simultaneously. Specifically, we propose a multitask vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in computation. We further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational complexity. Experimentally, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark instances. We further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution data. The source code is available at: https://***/RoyalSkye/Routing-MVMoE.

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Modem Myoelectric Control - Is it Time to Change the Algorithmic Focus? 46

Modem Myoelectric Control - Is it Time to Change the Algorit...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Khushaba, Rami N. Samuel, Oluwarotimi W. Al-Timemy, Ali H. Li, Guanglin Transport for Nsw AlexandriaNSW Australia University of Derby School of Computing and Data Science Research Centre Derby United Kingdom University of Baghdad Department of Biomedical Eng. Al-Kkwarizmi College of Eng Baghdad Iraq Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (纸本)9798350371499

This paper explores the evolving landscape of Electromyogram (EMG) signal analysis, focusing on the growing prominence of deep learning (DL) algorithms for hand, wrist, and finger movement recognition. Such algorithms often come with high computational costs, potentially limiting the clinical translation on resource-limited devices and igniting more research on reduced complexity models. This prompts the question: is it time to shift the algorithmic focus in EMG pattern recognition, given the reported performance of some light-weight traditional or hybrid methods emphasizing synergy between different EMG signals? A comparative study is implemented between state-of-the-art deep learning extension for time series classification, denoted as Random Convolutional Kernel Transform (ROCKET), and simple, yet effective pattern recognition methods tailored to exploit basic forms of EMG signal synergies - Waveform Length Phasors (WLPHASOR), Root-Mean-Squared Phasor (RMSPHASOR), and the proposed novel Multi-Signal Waveform Length (MSWL). Tests are conducted on EMG data from 22 participants performing 11 hand and wrist movements using two EMG armbands (10 and 8 channels, respectively), utilizing the open-source LibEMG toolbox. Preliminary findings suggest that, while DL algorithms exhibit formidable capabilities, the performance gap with traditional EMG feature extraction methods may not be as substantial as anticipated. The observations of this study revealed no significant differences in average accuracies between ROCKET, WLPHASOR, and RMSPHASOR (87% average across participants). Furthermore, MSWL significantly enhances performance to 90%, and the combination of ROCKET+MSWL achieves 91% on average across all subjects. These findings challenge the narrative of DL dominance in EMG pattern recognition, urging a re-evaluation of the algorithmic focus and contributing valuable insights to the debate on information extraction from EMG signals. © 2024 IEEE.

关键词： Electromyography

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Modeling Route Representation With Mixed-Scale Hierarchical Transformer

Modeling Route Representation With Mixed-Scale Hierarchical ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hanyuan Zhang Yuqi Chen Xinyu Zhang Qize Jiang Liang Li Baihua Zheng Weiwei Sun School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Fudan University Shanghai Institute of Intelligent Electronics & Systems School of Computing and Information Systems Singapore Management University

Modeling route representation aims to obtain contextual representations of an entire route for various traffic-related tasks. In reality, spatial-temporal data often exhibits multi-scale characteristics, which are utilized by many studies to enhance their performance. However, there is still a lack of in-depth research on how to effectively incorporate the multi-scale spatial-temporal information into transformer structure to adequately model route representation. In this paper, we propose a novel hierarchical route representation framework called RouteMT, which effectively captures multi-scale spatial-temporal characteristics of routes and leverages a mixed-scale transformer architecture to fuse intra and interroute features. Experiments on real data confirm RouteMT’s superior performance and versatility.

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Dynamic Formation Tracking of Multi-agent Systems with Bounded Unknown Leader Input 5th

Dynamic Formation Tracking of Multi-agent Systems with Bou...

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5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Su, Piaoyi Dong, Xiwang Zhao, Qin Li, Qingdong Ren, Zhang School of Automation Science and Electronic Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Beijing Institute of Control and Electronics Technology Beijing100038 China

ISBN: (纸本)9789811939976

This literature investigate the dynamic formation tracking problem of multiple Lagrange systems with linear nonautonomous leader over directed topology. In order to estimate the leader system, a distributed control scheme is proposed firstly by constructing adaptive distributed observer. Secondly, this paper design a nonlinear function to decrease the influence from the leader’s bounded unknown input. Then, the dynamic formation tracking problem of multiple Euler-Lagrange systems with nonlinear uncertainties and disturbances is solved by distributed tracking algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks

arXiv

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arXiv 2024年

作者： Wang, Yuwen Liu, Shunyu Zheng, Tongya Chen, Kaixuan Song, Mingli Institute of Blockchain and Data Security Hangzhou China Institute of Blockchain and Data Security Hangzhou China Big Graph Center School of Computer and Computing Science Hangzhou City University College of Computer Science and Technology Zhejiang University Hangzhou China

Graph Neural Networks (GNNs) have emerged as a prominent framework for graph mining, leading to significant advances across various domains. Stemmed from the node-wise representations of GNNs, existing explanation studies have embraced the subgraph-specific viewpoint that attributes the decision results to the salient features and local structures of nodes. However, graph-level tasks necessitate long-range dependencies and global interactions for advanced GNNs, deviating significantly from subgraph-specific explanations. To bridge this gap, this paper proposes a novel intrinsically interpretable scheme for graph classification, termed as Global Interactive Pattern (GIP) learning, which introduces learnable global interactive patterns to explicitly interpret decisions. GIP first tackles the complexity of interpretation by clustering numerous nodes using a constrained graph clustering module. Then, it matches the coarsened global interactive instance with a batch of self-interpretable graph prototypes, thereby facilitating a transparent graph-level reasoning process. Extensive experiments conducted on both synthetic and real-world benchmarks demonstrate that the proposed GIP yields significantly superior interpretability and competitive performance to the state-of-the-art counterparts. Our code will be made publicly available1 Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miao, Zibo Pan, Yu Gao, Qing School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Guangdong Key Laboratory of Intelligent Morphing Mechanisms Shenzhen518055 China The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique. © 2024 IEEE.

关键词： Poles

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