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6th International conference on communication and computational technologies, ICCCT 2024

作者： Vasudevan, Vibhav Ramakrishnan, Srinivas Seth, Utkarsh Shreya, M.B. Shylaja, S.S. Center for Data Science and Applied Machine Learning RR Campus Karnataka Bengaluru India

ISBN: (纸本)9789819774258

Video anomaly detection (VAD) is a demanding task because the very definition of anomalies in videos is inherently inconclusive and also due to the high manpower required to supervise lengthy videos. This research paper introduces a novel method for anomaly detection in videos. It utilizes the concurrent output of two deep learning models: the Convolutional Autoencoder (Conv-AE) for anomaly detection based on reconstruction errors and the Convolutional Long Short-Term Memory (ConvLSTM) for future frame prediction. The Conv-AE detects anomalies by capitalizing on its excellent spatial learning capabilities and the ConvLSTM model is helpful owing to its powerful temporal modeling abilities. By running these two models in parallel and normalizing the results obtained from both, we found that our combined model (CAELSTM) gave satisfactory results (AUROC) for two of the most prevalent datasets in this field of VAD, namely CUHK Avenue (77.44%) and Ped2 (87.31%), showcasing its promising performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

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Intelligent Assistant for Multivariant Analysis 26

Intelligent Assistant for Multivariant Analysis

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26th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2024

作者： Angerri, Xavier Delgado, Oscar Gibert, Karina Knowledge Engineering and Machine Learning Group Intelligent Data Science and Artificial Intelligence Research Center Universtitat Politècnica de Catalunya Spain

ISBN: (纸本)9781643685434

When a Knowledge Discovery from data (KDD) (Fayyad, Piatetsky-Shapiro, & Smyth, 1996) process is being applied to get knowledge, several methods could be used (Gibert, et al., 2018). A simple and fast way to obtain preliminary insights from data before using KDD models is by generating a basic descriptive analysis. It is one of the most popular ways to describe experimental data and should be the beginning of all data projects. Nevertheless some of the main knowledge that can be extracted in a descriptive analysis is hidden due to underlying multivariate structures which could be elicited through multivariate analysis techniques. Moreover, the domain expert is key for a proper interpretation of descriptive results. At the same time, there is a lack of automatic reporting techniques that can report and help in the interpretation of complex patterns and the use of advanced multivariate techniques. This paper shows the tool developed to generate automatic interpretation of Multiple Correspondence Analysis (MCA) and Principal Components Analysis (PCA) by using RMarkdown. This tool generates a Word document which contains the automatic interpretation of the results, built on the basis of regular expressions ellaborating over the R analytical outputs (either numerical or graphical results). The proposal is being applied with some real data, like INSESS database on social vulnerabilities of the Catalan population. In conclusion, the developed tool contributes to facilitate the factorial methods results, avoiding the misinterpretation of the results and the involuntary skipping of conclusions due to the large amount of knowledge that can be extracted from a complete factorial analysis. Also, this software enables non-expert users to read multivariate analysis results in a friendly way. Moreover, this tool saves time in the interpretation step and is a basis to support the expert to start the report with the results, even the output of the software could become the report or

关键词： automatic interpretation Automatic reporting explainability

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Urban Land Cover Classification with Efficient Hybrid Quantum machine learning Model 13

Urban Land Cover Classification with Efficient Hybrid Quantu...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Fan, Fan Shi, Yilei Zhu, Xiao Xiang Data Science in Earth Observation Munich Germany Wessling Germany Munich Germany Munich Center for Machine Learning Munich Germany

ISBN: (纸本)9798350308365

Urban land cover classification aims to derive crucial information from earth observation data and categorize it into specific land uses. To achieve accurate classification, sophisticated machine learning models trained with large earth observation data are employed, but the required computation power has become a bottleneck. Quantum computing might tackle this challenge in the future. However, representing images into quantum states for analysis with quantum computing is challenging due to the high demand for quantum resources. To tackle this challenge, we propose a hybrid quantum neural network that can effectively represent and classify remote sensing imagery with reduced quantum resources. Our model was evaluated on the Local Climate Zone (LCZ)-based land cover classification task using the TensorFlow Quantum platform, and the experimental results indicate its validity for accurate urban land cover classification. © 2024 IEEE.

关键词： Quantum computers

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Towards Highly Efficient Anomaly Detection for Predictive Maintenance 23

Towards Highly Efficient Anomaly Detection for Predictive Ma...

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23rd IEEE International Conference on machine learning and Applications, ICMLA 2024

作者： Klüttermann, Simon Peka, Vanlal Doebler, Philipp Müller, Emmanuel Tu Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Research Center Trustworthy Data Science and Security Dortmund Germany

ISBN: (纸本)9798350374889

This paper introduces SEAN, a novel anomaly detection algorithm designed for real-time applications in predictive maintenance. SEAN leverages an ensemble-based approach to deliver competitive performance while drastically reducing computational costs. In our comprehensive evaluation across 121 datasets, SEAN consistently outperforms comparable shallow anomaly detection algorithms. Our comparisons reveal that SEAN operates over 20,000 times faster than a similar state-of-the-art deep learning alternative, with negligible sacrifice in detection accuracy. We further demonstrate SEAN's versatility through an ablation study, highlighting how its hyperparameters can be tuned to balance runtime and performance effectively. Additionally, we present a practical C++ export tool that enables the deployment of SEAN on resource-constrained devices, meeting the stringent requirements of on-device predictive maintenance tasks. Our findings underscore SEAN as a powerful and efficient solution for anomaly detection in real-world engineering applications. © 2024 IEEE.

关键词： Predictive maintenance

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Create! Don’t Repeat: A Paradigm Shift in Multi-Label Augmentation through Label Creative Generation

Create! Don’t Repeat: A Paradigm Shift in Multi-Label Augme...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Wang, Letian Liu, Xianggen Lv, Jiancheng College of Computer Science Sichuan University China Engineering Research Center of Machine Learning and Industry Intelligence China

ISBN: (纸本)9798891761148

We propose Label Creative Generation (LCG), a new paradigm in multi-label data augmentation. Beyond repeating data points with fixed labels, LCG creates new data by exploring innovative label combinations. Within LCG, we introduce Tail-Driven Conditional Augmentation (TDCA), combining tail-driven label sampling and label-conditioned text generation for balanced, consistent data augmentation. Our approach has demonstrated a 100.21% increase in PSP@1 across three datasets, successfully mitigating the long-tail effect in MLTC and markedly enhancing model performance. © 2024 Association for Computational Linguistics.

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An Improved Finite-time Analysis of Temporal Difference learning with Deep Neural Networks 41

An Improved Finite-time Analysis of Temporal Difference Lear...

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41st International Conference on machine learning, ICML 2024

作者： Ke, Zhifa Wen, Zaiwen Zhang, Junyu Center for Data Science Peking University China Beijing International Center for Mathematical Research Center for Machine Learning Research Changsha Institute for Computing and Digital Economy Beijing China Department of Industrial Systems Engineering and Management National University of Singapore Singapore

Temporal difference (TD) learning algorithms with neural network function parameterization have well-established empirical success in many practical large-scale reinforcement learning tasks. However, theoretical understanding of these algorithms remains challenging due to the nonlinearity of the action-value approximation. In this paper, we develop an improved non-asymptotic analysis of the neural TD method with a general L-layer neural network. New proof techniques are developed and an improved new Õ(ϵ-1) sample complexity is derived. To our best knowledge, this is the first finite-time analysis of neural TD that achieves an Õ(ϵ-1) complexity under the Markovian sampling, as opposed to the best known Õ(ϵ-2) complexity in the existing literature. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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learning Invariance Preserving Moment Closure Model for Boltzmann-BGK Equation

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Communications in Mathematics and Statistics 2023年第1期11卷 59-101页

作者： Zhengyi Li Bin Dong Yanli Wang School of Mathematical Sciences Peking UniversityBeijingPeople’s Republic of China Beijing International Center for Mathematical Research&Center for Machine Learning Research Peking UniversityBeijingPeople’s Republic of China Beijing Computational Science Research Center BeijingPeople’s Republic of China

As one of the main governing equations in kinetic theory,the Boltzmann equation is widely utilized in aerospace,microscopic flow,*** high-resolution simulation is crucial in these related ***,due to the high dimensionality of the Boltzmann equation,high-resolution simulations are often difficult to achieve *** moment method which was first proposed in Grad(Commun Pure Appl Math 2(4):331-407,1949)is among the popular numerical methods to achieve efficient high-resolution *** can derive the governing equations in the moment method by taking moments on both sides of the Boltzmann equation,which effectively reduces the dimensionality of the ***,one of themain challenges is that it leads to an unclosed moment system,and closure is needed to obtain a closedmoment *** is truly an art in designing closures for moment systems and has been a significant research field in kinetic *** than the traditional human designs of closures,the machine learning-based approach has attracted much attention lately in Han et al.(Proc Natl Acad Sci USA 116(44):21983-21991,2019)and Huang et al.(J Non-Equilib Thermodyn 46(4):355-370,2021).In this work,we propose a machine learning-based method to derive a moment closure model for the Boltzmann-BGK *** particular,the closure relation is approximated by a carefully designed deep neural network that possesses desirable physical invariances,i.e.,the Galilean invariance,reflecting invariance,and scaling invariance,inherited from the original Boltzmann-BGK equation and playing an important role in the correct simulation of the Boltzmann *** simulations on the 1D-1D examples including the smooth and discontinuous initial condition problems,Sod shock tube problem,the shock structure problems,and the 1D-3D examples including the smooth and discontinuous problems demonstrate satisfactory numerical performances of the proposed invariance preserving neural closure method

关键词： Boltzmann equation Moment closure machine learning Neural networks Invariance preserving

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Improving Generalization and Convergence by Enhancing Implicit Regularization 38

Improving Generalization and Convergence by Enhancing Implic...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Mingze Wang, Jinbo He, Haotian Wang, Zilin Huang, Guanhua Xiong, Feiyu Li, Zhiyu Weinan, E. Wu, Lei School of Mathematical Sciences Peking University China Center for Machine Learning Research Peking University China China AI for Science Institute China School of Data Science University of Science and Technology of China China ByteDance Research China

In this work, we propose an Implicit Regularization Enhancement (IRE) framework to accelerate the discovery of flat solutions in deep learning, thereby improving generalization and convergence. Specifically, IRE decouples the dynamics of flat and sharp directions, which boosts the sharpness reduction along flat directions while maintaining the training stability in sharp directions. We show that IRE can be practically incorporated with generic base optimizers without introducing significant computational overload. Experiments show that IRE consistently improves the generalization performance for image classification tasks across a variety of benchmark datasets (CIFAR-10/100, ImageNet) and models (ResNets and ViTs). Surprisingly, IRE also achieves a 2× speed-up compared to AdamW in the pre-training of Llama models (of sizes ranging from 60M to 229M) on datasets including Wikitext-103, Minipile, and Openwebtext. Moreover, we provide theoretical guarantees, showing that IRE can substantially accelerate the convergence towards flat minima in sharpness-aware minimization (SAM). © 2024 Neural information processing systems foundation. All rights reserved.

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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DUO: Diverse, Uncertain, On-Policy Query Generation and Selection for Reinforcement learning from Human Feedback 39

DUO: Diverse, Uncertain, On-Policy Query Generation and Sele...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Feng, Xuening Jiang, Zhaohui Kaufmann, Timo Xu, Puchen Hüllermeier, Eyke Weng, Paul Zhu, Yifei UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai China Institute for Informatics LMU Munich Munich Germany Munich Center of Machine Learning Munich Germany German Research Center for Artificial Intelligence Germany Data Science Research Center Duke Kunshan University Kunshan China

ISBN: (纸本)157735897X

Defining a reward function is usually a challenging but critical task for the system designer in reinforcement learning, especially when specifying complex behaviors. Reinforcement learning from human feedback (RLHF) emerges as a promising approach to circumvent this. In RLHF, the agent typically learns a reward function by querying a human teacher using pairwise comparisons of trajectory segments. A key question in this domain is how to reduce the number of queries necessary to learn an informative reward function, since asking a human teacher too many queries is impractical and costly. To tackle this question, most existing methods mainly focus on improving exploration, introducing data augmentation or designing sophisticated training objectives for RLHF, while the potential of query generation and selection schemes have not been fully exploited. In this paper, we propose DUO, a novel method for diverse, uncertain, on-policy query generation and selection in RLHF. Our method produces queries that are (1) more relevant for policy training (via an on-policy criterion), (2) more informative (via a principled measure of epistemic uncertainty), and (3) diverse (via a clustering-based filter). Experimental results on a variety of locomotion and robotic manipulation tasks demonstrate that our method can outperform state-of-the-art RLHF methods given the same total budget of queries while being robust to possibly irrational teachers. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

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