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RandD activities at machine learning and data science center

NTT Technical Review 2016年第2期14卷

作者： Ueda, Naonori Deprtment of Machine Learning and Data Science Center Senior Distinguished Scientist NTT Communication Science Laboratories Japan

The machine learning and data science center (MLC) was established in April 2013 as a research and development hub of big data analysis technologies at NTT laboratories with the aim of creating innovative services from a wide variety of big data. MLC uses machine learning and data mining technologies cultivated by NTT laboratories and a parallel-distributed processing platform (Jubatus) for high-efficiency and real-Time data analysis to develop diverse big data analysis technologies and support big data services. This article introduces these big data activities at MLC.

关键词： Big data

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Parallelizing Video Anomaly Detection Using Reconstruction and Future Frame Prediction 6th

Parallelizing Video Anomaly Detection Using Reconstruction a...

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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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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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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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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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The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks 38

The Map Equation Goes Neural: Mapping Network Flows with Gra...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Blöcker, Christopher Tan, Chester Scholtes, Ingo Data Analytics Group Department of Informatics University of Zurich Switzerland Machine Learning for Complex Networks Center for Artificial Intelligence and Data Science Julius-Maximilians-Universität Würzburg Germany

Community detection is an essential tool for unsupervised data exploration and revealing the organisational structure of networked systems. With a long history in network science, community detection typically relies on objective functions, optimised with custom-tailored search algorithms, but often without leveraging recent advances in deep learning. Recently, first works have started incorporating such objectives into loss functions for deep graph clustering and pooling. We consider the map equation, a popular information-theoretic objective function for unsupervised community detection, and express it in differentiable tensor form for optimisation through gradient descent. Our formulation turns the map equation compatible with any neural network architecture, enables end-to-end learning, incorporates node features, and chooses the optimal number of clusters automatically, all without requiring explicit regularisation. Applied to unsupervised graph clustering tasks, we achieve competitive performance against state-of-the-art deep graph clustering baselines in synthetic and real-world datasets. © 2024 Neural information processing systems foundation. All rights reserved.

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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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CUTE: Measuring LLMs' Understanding of Their Tokens

CUTE: Measuring LLMs' Understanding of Their Tokens

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Edman, Lukas Schmid, Helmut Fraser, Alexander Center for Information and Language Processing LMU Munich Germany School of Computation Information and Technology TU Munich Germany Munich Center for Machine Learning Germany Munich Data Science Institute Germany

ISBN: (纸本)9798891761643

Large Language Models (LLMs) show remarkable performance on a wide variety of tasks. Most LLMs split text into multi-character tokens and process them as atomic units without direct access to individual characters. This raises the question: To what extent can LLMs learn orthographic information? To answer this, we propose a new benchmark, CUTE, which features a collection of tasks designed to test the orthographic knowledge of LLMs. We evaluate popular LLMs on CUTE, finding that most of them seem to know the spelling of their tokens, yet fail to use this information effectively to manipulate text, calling into question how much of this knowledge is generalizable. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Digital Halftoning via Mixed-Order Weighted ΣΔ Modulation

Digital Halftoning via Mixed-Order Weighted ΣΔ Modulation

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2023 International Conference on Sampling Theory and Applications, SampTA 2023

作者： Krahmer, Felix Veselovska, Anna Technical University of Munich and Munich Center for Machine Learning Dept. of Mathematics & Munich Data Science Institute Garching/Munich Germany

ISBN: (纸本)9798350328851

In this paper, we propose 1-bit weighted Σ quantization schemes of mixed order as a technique for digital halftoning. These schemes combine weighted Σ schemes of different orders for two-dimensional signals so one can profit both from the better stability properties of low order schemes and the better accuracy properties of higher order schemes. We demonstrate that the resulting mixed-order Σ schemes in combination with a padding strategy yield improved representation quality in digital halftoning as measured in the Feature Similarity *** empirical results are complemented by mathematical error bounds for the model of two-dimensional bandlimited signals as motivated by a mathematical model of human visual perception. © 2023 IEEE.

关键词： Stability

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Expected Probabilistic Hierarchies 38

Expected Probabilistic Hierarchies

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kollovieh, Marcel Charpentier, Bertrand Zügner, Daniel Günnemann, Stephan School of Computation Information and Technology Technical University of Munich Germany Munich Data Science Institute Germany Munich Center for Machine Learning Germany Pruna AI Germany Microsoft Research AI for Science United States

Hierarchical clustering has usually been addressed by discrete optimization using heuristics or continuous optimization of relaxed scores for hierarchies. In this work, we propose to optimize expected scores under a probabilistic model over hierarchies. (1) We show theoretically that the global optimal values of the expected Dasgupta cost and Tree-Sampling divergence (TSD), two unsupervised metrics for hierarchical clustering, are equal to the optimal values of their discrete counterparts contrary to some relaxed scores. (2) We propose Expected Probabilistic Hierarchies (EPH), a probabilistic model to learn hierarchies in data by optimizing expected scores. EPH uses differentiable hierarchy sampling enabling end-to-end gradient descent based optimization, and an unbiased subgraph sampling approach to scale to large datasets. (3) We evaluate EPH on synthetic and real-world datasets including vector and graph datasets. EPH outperforms all other approaches quantitatively and provides meaningful hierarchies in qualitative evaluations. © 2024 Neural information processing systems foundation. All rights reserved.

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