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检索条件"机构=Machine Learning and Data Analytics Group"
52 条 记 录,以下是21-30 订阅
排序:
Beyond the Known: Adversarial Autoencoders in Novelty Detection
arXiv
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arXiv 2024年
作者: Asad, Muhammad Ullah, Ihsan Sistu, Ganesh Madden, Michael G. Machine Learning Research Group School of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Ireland Valeo Vision Systems Tuam Ireland
In novelty detection, the goal is to decide if a new data point should be categorized as an inlier or an outlier, given a training dataset that primarily captures the inlier distribution. Recent approaches typically u... 详细信息
来源: 评论
Depth Estimation using Weighted-loss and Transfer learning
arXiv
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arXiv 2024年
作者: Hafeez, Muhammad Adeel Madden, Michael G. Sistu, Ganesh Ullah, Ihsan Machine Learning Research Group School of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Ireland Valeo Vision Systems Tuam Ireland
Depth estimation from 2D images is a common computer vision task that has applications in many fields including autonomous vehicles, scene understanding and robotics. The accuracy of a supervised depth estimation meth... 详细信息
来源: 评论
Estimating the robustness of classification models by the structure of the learned feature-space
arXiv
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arXiv 2021年
作者: Ho, Kalun Pfreundt, Franz-Josef Keuper, Janis Keuper, Margret CC-HPC Fraunhofer ITWM Fraunhofer-Platz 1 Kaiserslautern67663 Germany Institute for Machine Learning and Analytics Offenburg University Germany Data and Web Science Group University of Mannheim Germany
Over the last decade, the development of deep image classification networks has mostly been driven by the search for the best performance in terms of classification accuracy on standardized benchmarks like ImageNet. M... 详细信息
来源: 评论
learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches
Learning Embeddings for Image Clustering: An Empirical Study...
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International Conference on Pattern Recognition
作者: Kalun Ho Janis Keuper Franz-Josef Pfreundt Margret Keuper Data and Web Science Group University of Mannheim Germany Institute for Machine Learning and Analytics (IMLA) Offenburg University Germany Competence Center High Performance Computing Fraunhofer ITWM Kaiserslautern Germany
In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolution... 详细信息
来源: 评论
Predicting Influential Higher-Order Patterns in Temporal Network data
arXiv
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arXiv 2021年
作者: Gote, Christoph Perri, Vincenzo Scholtes, Ingo Data Analytics Group University of Zurich Zurich Switzerland Chair of Systems Design ETH Zurich Zurich Switzerland Chair of Machine Learning for Complex Networks University of Würzburg Würzburg Germany
Networks are frequently used to model complex systems comprised of interacting elements. While edges capture the topology of direct interactions, the true complexity of many systems originates from higher-order patter... 详细信息
来源: 评论
Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions
arXiv
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arXiv 2020年
作者: Durall, Ricard Keuper, Margret Keuper, Janis Competence Center High Performance Computing Fraunhofer ITWM Kaiserslautern Germany Data- and Webscience Group University Mannheim Germany IWR University of Heidelberg Germany Institute for Machine Learning and Analytics Offenburg University Germany
Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show ... 详细信息
来源: 评论
Predicting Influential Higher-Order Patterns in Temporal Network data
Predicting Influential Higher-Order Patterns in Temporal Net...
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International Conference on Advances in Social Network Analysis and Mining, ASONAM
作者: Christoph Gote Vincenzo Perri Ingo Scholtes Data Analytics Group University of Zurich Zurich Switzerland Chair of Systems Design ETH Zurich Switzerland Chair of Machine Learning for Complex Networks Julius-Maximilians-Universität Würzburg Würzburg Germany
Networks are frequently used to model complex systems comprised of interacting elements. While edges capture the topology of direct interactions, the true complexity of many systems originates from higher-order patter... 详细信息
来源: 评论
Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions
Watch Your Up-Convolution: CNN Based Generative Deep Neural ...
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Conference on Computer Vision and Pattern Recognition (CVPR)
作者: Ricard Durall Margret Keuper Janis Keuper Competence Center High Performance Computing Fraunhofer ITWM Kaiserslautern Germany IWR University of Heidelberg Germany Data and Web Science Group University of Mannheim Germany Institute for Machine Learning and Analytics Offenburg University Germany
Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show ... 详细信息
来源: 评论
A Network Perspective on the Influence of Code Review Bots on the Structure of Developer Collaborations
arXiv
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arXiv 2023年
作者: Röseler, Leonore Scholtes, Ingo Gote, Christoph Data Analytics Group University of Zurich Zurich Switzerland Social Computing Group University of Zurich Zurich Switzerland Machine Learning for Complex Networks Julius-Maximilians-Universität Würzburg Würzburg Germany Systems Design ETH Zurich Zurich Switzerland
Background: Despite a growing body of literature on the impact of software bots on open source software development teams, their effects on team communication, coordination, and collaboration practices are not well un... 详细信息
来源: 评论
Hybridizing Target- and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization
arXiv
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arXiv 2024年
作者: Dietrich, Konstantin Prager, Raphael Patrick Doerr, Carola Trautmann, Heike Big Data Analytics in Transportation TU Dresden Germany ScaDS.AI Dresden Germany Data Science: Statistics and Optimization University of Münster Germany Sorbonne Université CNRS LIP6 Paris France Machine Learning and Optimisation Paderborn University Germany Data Management and Biometrics Group University of Twente Netherlands
Exploratory landscape analysis (ELA) is a well-established tool to characterize optimization problems via numerical features. ELA is used for problem comprehension, algorithm design, and applications such as automated... 详细信息
来源: 评论