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arXiv 2023年

作者： Moss, Guy Višnjević, Vjeran Eisen, Olaf Oraschewski, Falk M. Schröder, Cornelius Macke, Jakob H. Drews, Reinhard Machine Learning in Science University of Tübingen Tübingen AI Center Germany Department of Geosciences University of Tübingen Germany Alfred-Wegener-Institut Helmholtz-Zentrum für Polar und Meeresforschung Bremerhaven Germany Department of Geosciences Universität Bremen Germany Max Planck Institute for Intelligent Systems Tübingen Germany

The ice shelves buttressing the Antarctic ice sheet determine the rate of ice-discharge into the surrounding oceans. The geometry of ice shelves, and hence their buttressing strength, is determined by ice flow as well as by the local surface accumulation and basal melt rates, governed by atmospheric and oceanic conditions. Contemporary methods resolve one of these rates, but typically not both. Moreover, there is little information of how they changed in time. We present a new method to simultaneously infer the surface accumulation and basal melt rates averaged over decadal and centennial timescales. We infer the spatial dependence of these rates along flow line transects using internal stratigraphy observed by radars, using a kinematic forward model of internal stratigraphy. We solve the inverse problem using simulation-based inference (SBI). SBI performs Bayesian inference by training neural networks on simulations of the forward model to approximate the posterior distribution, allowing us to also quantify uncertainties over the inferred parameters. We demonstrate the validity of our method on a synthetic example, and apply it to Ekström Ice Shelf, Antarctica, for which newly acquired radar measurements are available. We obtain posterior distributions of surface accumulation and basal melt averaging over 42, 84, 146, and 188 years before 2022. Our results suggest stable atmospheric and oceanographic conditions over this period in this catchment of Antarctica. Use of observed internal stratigraphy can separate the effects of surface accumulation and basal melt, allowing them to be interpreted in a historical context of the last centuries and beyond. © 2023, CC BY.

关键词： Inverse problems

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Robustness in Fatigue Strength Estimation

arXiv

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arXiv 2022年

作者： Weichert, Dorina Kister, Alexander Houben, Sebastian Ernis, Gunar Wrobel, Stefan Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Germany German Federal Institute for Materials Research and Testing Section S.3 eScience Germany University of Applied Sciences Bonn-Rhein-Sieg Germany Fraunhofer Center for Machine Learning Germany

Fatigue strength estimation is a costly manual material characterization process in which state-of-the-art approaches follow a standardized experiment and analysis procedure. In this paper, we examine a modular, machine learning-based approach for fatigue strength estimation that is likely to reduce the number of experiments and, thus, the overall experimental costs. Despite its high potential, deployment of a new approach in a real-life lab requires more than the theoretical definition and simulation. Therefore, we study the robustness of the approach against misspecification of the prior and discretization of the specified loads. We identify its applicability and its advantageous behavior over the state-of-the-art methods, potentially reducing the number of costly experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： machine learning

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Bayesian optimization for min max optimization

arXiv

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arXiv 2021年

作者： Weichert, Dorina Kister, Alexander Fraunhofer Center for Machine Learning Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Schloss Birlinghoven Sankt Augustin53757 Germany Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Schloss Birlinghoven Sankt Augustin53757 Germany

A solution that is only reliable under favourable conditions is hardly a safe solution. Min Max Optimization is an approach that returns optima that are robust against worst case conditions. We propose algorithms that perform Min Max Optimization in a setting where the function that should be optimized is not known a priori and hence has to be learned by experiments. Therefore we extend the Bayesian Optimization setting, which is tailored to maximization problems, to Min Max Optimization problems. While related work extends the two acquisition functions Expected Improvement and Gaussian Process Upper Confidence Bound;we extend the two acquisition functions Entropy Search and Knowledge Gradient. These acquisition functions are able to gain knowledge about the optimum instead of just looking for points that are supposed to be optimal. In our evaluation we show that these acquisition functions allow for better solutions-converging faster to the optimum than the benchmark settings. Copyright © 2021, The Authors. All rights reserved.

关键词： machine learning

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On the effects of biased quantum random numbers on the initialization of artificial neural networks

arXiv

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arXiv 2021年

作者： Heese, Raoul Wolter, Moritz Mücke, Sascha Franken, Lukas Piatkowski, Nico Fraunhofer Center for Machine Learning and Fraunhofer Institute for Industrial Mathematics ITWM Germany Fraunhofer Center for Machine Learning and Fraunhofer Institute for Algorithms and Scientific Computing SCAI Germany Artificial Intelligence Group TU Dortmund University Germany Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Germany

Recent advances in practical quantum computing have led to a variety of cloud-based quantum computing platforms that allow researchers to evaluate their algorithms on noisy intermediate-scale quantum (NISQ) devices. A common property of quantum computers is that they can exhibit instances of true randomness as opposed to pseudo-randomness obtained from classical systems. Investigating the effects of such true quantum randomness in the context of machine learning is appealing, and recent results vaguely suggest that benefits can indeed be achieved from the use of quantum random numbers. To shed some more light on this topic, we empirically study the effects of hardware-biased quantum random numbers on the initialization of artificial neural network weights in numerical experiments. We find no statistically significant difference in comparison with unbiased quantum random numbers as well as biased and unbiased random numbers from a classical pseudo-random number generator. The quantum random numbers for our experiments are obtained from real quantum hardware. Copyright © 2021, The Authors. All rights reserved.

关键词： Random number generation

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Validation of simulation-based testing: Bypassing domain shift with label-to-image synthesis

arXiv

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arXiv 2021年

作者： Rosenzweig, Julia Brito, Eduardo Kobialka, Hans-Ulrich Akila, Maram Schmidt, Nico M. Schlicht, Peter Schneider, Jan David Hüger, Fabian Rottmann, Matthias Houben, Sebastian Wirtz, Tim Fraunhofer Institute for Intelligent Analysis and Information Systems Fraunhofer Center for Machine Learning Sankt Augustin Germany Cariad Se Volkswagen Ag University of Wuppertal Dept. of Mathematics

Many machine learning applications can benefit from simulated data for systematic validation - in particular if real-life data is difficult to obtain or annotate. However, since simulations are prone to domain shift w.r.t. real-life data, it is crucial to verify the transferability of the obtained results. We propose a novel framework consisting of a generative label-to-image synthesis model together with different transferability measures to inspect to what extent we can transfer testing results of semantic segmentation models from synthetic data to equivalent real-life data. With slight modifications, our approach is extendable to, e.g., general multi-class classification tasks. Grounded on the transferability analysis, our approach additionally allows for extensive testing by incorporating controlled simulations. We validate our approach empirically on a semantic segmentation task on driving scenes. Transferability is tested using correlation analysis of IoU and a learned discriminator. Although the latter can distinguish between reallife and synthetic tests, in the former we observe surprisingly strong correlations of 0.7 for both cars and pedestrians. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Planning Under Partial Observability: A Study in High-Precision Manufacturing

SSRN

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SSRN 2022年

作者： Weichert, Dorina Kister, Alexander Houben, Sebastian Volbach, Peter Trost, Marcus Hartung, Johannes Bergner, Alexander Wrobel, Stefan Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Sankt Augustin Germany Berlin Germany University of Applied Sciences Bonn-Rhein-Sieg Sankt Augustin Germany Fraunhofer Institute for Applied Optics and Precision Engineering IOF Jena Germany Fraunhofer Center for Machine Learning Germany

Conceptually, high-precision manufacturing is a sequence of production and measurement steps, where both kinds of steps lead to non-deterministic results due to production and measurement tolerances. In this paper, we demonstrate how to effectively represent these manufacturing processes as Partially Observable Markov Decision Process (POMDP) and derive an offline strategy with state-of-the-art Monte Carlo Tree Search (MCTS) approaches. In doing so, we face two challenges: a continuous observation space and explainability requirements from the side of the process engineers. As a result, we find a tradeoff between the quantitative performance of the solution and its explainability. In a nutshell, the paper elucidates the entire process for finding a reliable solution: We design and validate a white-box simulation from expert knowledge, examine state-of-the-art POMDP solvers, and discuss our results from both the perspective of state-of-the-art machine learning research and as an illustration for high-precision manufacturing practitioners. © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Overcoming common flaws in the evaluation of selective classification systems 24

Overcoming common flaws in the evaluation of selective class...

引用

Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Jeremias Traub Till J. Bungert Carsten T. Lüth Michael Baumgartner Klaus H. Maier-Hein Lena Maier-Hein Paul F. Jäger German Cancer Research Center (DKFZ) Heidelberg Interactive Machine Learning Group Germany and Helmholtz Imaging DKFZ Heidelberg Germany German Cancer Research Center (DKFZ) Heidelberg Interactive Machine Learning Group Germany and Helmholtz Imaging DKFZ Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany Helmholtz Imaging DKFZ Heidelberg Germany and DKFZ Heidelberg Division of Medical Image Computing (MIC) Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany Helmholtz Imaging DKFZ Heidelberg Germany and DKFZ Heidelberg Division of Medical Image Computing (MIC) Germany and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany and National Center for Tumor Diseases (NCT) Heidelberg Helmholtz Imaging DKFZ Heidelberg Germany and DKFZ Heidelberg Division of Intelligent Medical Systems (IMSY) Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany and National Center for Tumor Diseases (NCT) Heidelberg

ISBN: (纸本)9798331314385

Selective Classification, wherein models can reject low-confidence predictions, promises reliable translation of machine-learning based classification systems to real-world scenarios such as clinical diagnostics. While current evaluation of these systems typically assumes fixed working points based on pre-defined rejection thresholds, methodological progress requires benchmarking the general performance of systems akin to the AUROC in standard classification. In this work, we define 5 requirements for multi-threshold metrics in selective classification regarding task alignment, interpretability, and flexibility, and show how current approaches fail to meet them. We propose the Area under the Generalized Risk Coverage curve (AUGRC), which meets all requirements and can be directly interpreted as the average risk of undetected failures. We empirically demonstrate the relevance of AUGRC on a comprehensive benchmark spanning 6 data sets and 13 confidence scoring functions. We find that the proposed metric substantially changes metric rankings on 5 out of the 6 data sets.

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Provable Tensor Completion with Graph Information

arXiv

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arXiv 2023年

作者： Wang, Kaidong Wang, Yao Liao, Xiuwu Tang, Shaojie Yang, Can Meng, Deyu Center for Intelligent Decision-making and Machine Learning School of Management Xian Jiaotong University Shaan’xi Xi’an China Naveen Jindal School of Management The University of Texas at Dallas RichardsonTX United States Department of Mathematics The Hong Kong University of Science and Technology Hong Kong School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xian Jiaotong University Shaan’xi Xian China Macau Institute of Systems Engineering Macau University of Science and Technology Taipa China

Graphs, depicting the interrelations between variables, has been widely used as effective side information for accurate data recovery in various matrix/tensor recovery related applications. In this paper, we study the tensor completion problem with graph information. Current research on graph-regularized tensor completion tends to be task-specific, lacking generality and systematic approaches. Furthermore, a recovery theory to ensure performance remains absent. Moreover, these approaches overlook the dynamic aspects of graphs, treating them as static akin to matrices, even though graphs could exhibit dynamism in tensor-related scenarios. To confront these challenges, we introduce a pioneering framework in this paper that systematically formulates a novel model, theory, and algorithm for solving the dynamic graph regularized tensor completion problem. For the model, we establish a rigorous mathematical representation of the dynamic graph, based on which we derive a new tensor-oriented graph smoothness regularization. By integrating this regularization into a tensor decomposition model based on transformed t-SVD, we develop a comprehensive model simultaneously capturing the low-rank and similarity structure of the tensor. In terms of theory, we showcase the alignment between the proposed graph smoothness regularization and a weighted tensor nuclear norm. Subsequently, we establish assurances of statistical consistency for our model, effectively bridging a gap in the theoretical examination of the problem involving tensor recovery with graph information. In terms of the algorithm, we develop a solution of high effectiveness, accompanied by a guaranteed convergence, to address the resulting model. To showcase the prowess of our proposed model in contrast to established ones, we provide in-depth numerical experiments encompassing synthetic data as well as real-world datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Tensors

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Analysis of the Research Status of Information Technology Education Literature in China and Abroad

Analysis of the Research Status of Information Technology Ed...

引用

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yulin Zhao Kai Liu Junke Li School of Computer and Information Qiannan Normal University for Nationalities Duyun China Key Laboratory of Machine learning and Unstructured Data Processing of Qiannan Duyun China School of Information Engineering Suqian University Suqian Jiangsu China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun China Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment Jiangsu China

Information technology education contributes to the development of national education. Understanding the research status of information technology education at home and abroad is helpful to the implementation of education informatization. This paper takes the information technology education research literature collected by CNKI and the Web of Science database as the object and uses the bibliometric method to analyze the literature characteristics of information technology education at home and abroad. Specifically, the paper mainly carries out statistical analysis from the time distribution of literature, journal distribution, and hot keywords, and visually describes the literature features of the information technology education discipline, providing references for the research of information technology education discipline at home and abroad.

关键词： Statistical analysis Databases Education Bibliometrics Educational technology Market research Internet

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GSLB: the graph structure learning benchmark 23

GSLB: the graph structure learning benchmark

引用

Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

Graph Structure learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.

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