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arXiv 2021年

作者： Roth, Benjamin Çano, Erion Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria

We propose a scheme for self-training of grammaticality models for constituency analysis based on linguistic tests. A pre-trained language model is fine-tuned by contrastive estimation of grammatical sentences from a corpus, and ungrammatical sentences that were perturbed by a syntactic test, a transformation that is motivated by constituency theory. We show that consistent gains can be achieved if only certain positive instances are chosen for training, depending on whether they could be the result of a test transformation. This way, the positives, and negatives exhibit similar characteristics, which makes the objective more challenging for the language model, and also allows for additional markup that indicates the position of the test application within the sentence. Copyright © 2021, The Authors. All rights reserved.

关键词： Computational linguistics

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DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids

DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Ai...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Tsangko, Iosif Triantafyllopoulos, Andreas Müller, Michael Schröter, Hendrik Schuller, Björn W. EIHW - Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Technical University of Munich Germany MCML - Munich Center for Machine Learning Munich Germany WS Audiology Research and Development Erlangen Germany GLAM - Group on Language Audio & Music Imperial College London United Kingdom MDSI - Munich Data Science Institute Munich Germany

ISBN: (纸本)9798350368741

The DeepFilterNet (DFN) architecture was recently proposed as a deep learning model suited for hearing aid devices. Despite its competitive performance on numerous benchmarks, it still follows a 'one-size-fits-all' approach, which aims to train a single, monolithic architecture that generalises across different noises and environments. However, its limited size and computation budget can hamper its generalisability. Recent work has shown that in-context adaptation can improve performance by conditioning the denoising process on additional information extracted from background recordings to mitigate this. These recordings can be offloaded outside the hearing aid, thus improving performance while adding minimal computational overhead. We introduce these principles to the DFN model, thus proposing the DFingerNet (DFiN) model, which shows superior performance on various benchmarks inspired by the DNS Challenge. © 2025 IEEE.

关键词： Context Adaptation Deep learning Hearing Aids Speech Denoising Speech Enhancement

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Design and Implementation of a data Governance Framework and Platform: A Case Study of a National research Organization of Thailand 20

Design and Implementation of a Data Governance Framework and...

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20th International Joint Conference on Computer Science and Software Engineering, JCSSE 2023

作者： Chanyachatchawan, Sapa Nasingkun, Krich Tumsangthong, Patipat Chata, Porntiwa Buranarach, Marut Socharoentum, Monsak National Electronics and Computer Technology Center Leveraging Technology Solutions Section Bangkok Thailand National Electronics and Computer Technology Center Strategic Analytics Networks with Machine Learning and Ai Research Bangkok Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Bangkok Thailand Digital Government Development Agency Bangkok Thailand

ISBN: (纸本)9798350300505

In the current era of extensive data usage across industries, data collection, preservation, utilization, and organization has become more challenging and nuanced because it is necessary to consider critical concerns such as data security, privacy, and legal issues, apart from efficiency issues. As a result, Thai government initiated the idea and effort to implement data governance throughout the government agency. This paper showcases the implementation of data governance in a governmental research organization with highly diverse structured and unstructured data. The implementation follows international standards and the guidelines of the Digital Government Development Agency (DGA). The executives set up the working body, including the data Governance Council and data Stewards, responsible for setting up and deploying policies and regulations. Creating awareness and the necessary infrastructure are the main focuses in the first-year phase. The metadata was designed to extend DGA's version and match the organization's unique requirements. A data catalog platform was developed accordingly. We organized activities to boost employee awareness and participation, including advertising and data catalog platform training. By the end of the first year of implementation, every organization unit had registered at least one data record into the data catalog. © 2023 IEEE.

关键词： Metadata

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Pattern Discovery in an EEG database of Depression Patients: Preliminary Results

Pattern Discovery in an EEG Database of Depression Patients:...

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International Conference on Measurement

作者： Kateřina Hlaváčková-Schindler Christina Pacher Claudia Plant Mykola Lazarenko Milan Paluš Jaroslav Hlinka Aditi Kathpalia Martin Brunovský Data Mining and Machine Learning Research Group Faculty of Computer Science University of Vienna Vienna Austria Department of Complex Systems Institute of Computer Science Czech Academy of Sciences Prague Czechia Clinical Research Programme National Institute of Mental Health Klecany Czechia

The ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4-6 weeks after starting current antidepressive medication. Based on the data basis of the patients and their EEG before and on the 7th day of treatment we apply data mining, causal discovery and machine learning approaches to discover interactive patterns between patient’s brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project "learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants" ongoing at the University of Vienna, the Czech Academy of Sciences and the National Institute of Mental Health in the Czech Republic.

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ReInform: Selecting paths with reinforcement learning for contextualized link prediction

arXiv

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arXiv 2022年

作者： Speranskaya, Marina Methias, Sameh Roth, Benjamin Center for Information and Language Processing LMU Munich Germany Technical University of Munich Munich Germany Research Group Data Mining and Machine Learning University of Vienna Vienna Austria

We propose to use reinforcement learning to inform transformer-based contextualized link prediction models by providing paths that are most useful for predicting the correct answer. This is in contrast to previous approaches, that either used reinforcement learning (RL) to directly search for the answer, or based their prediction on limited or randomly selected context. Our experiments on WN18RR and FB15k-237 show that contextualized link prediction models consistently outperform RL-based answer search, and that additional improvements (of up to 13.5% MRR) can be gained by combining RL with a link prediction model. The PyTorch implementation of the RL agent is available at https://***/marina-sp/reinform. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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xMIL: Insightful Explanations for Multiple Instance learning in Histopathology 38

xMIL: Insightful Explanations for Multiple Instance Learning...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hense, Julius Idaji, Mina Jamshidi Eberle, Oliver Schnake, Thomas Dippel, Jonas Ciernik, Laure Buchstab, Oliver Mock, Andreas Klauschen, Frederick Müller, Klaus-Robert Berlin Institute for the Foundations of Learning and Data Berlin Germany Machine Learning Group Technische Universität Berlin Berlin Germany Aignostics GmbH Berlin Germany Institute of Pathology Ludwig Maximilian University Munich Germany German Cancer Research Center Heidelberg Germany German Cancer Consortium Munich Germany Institute of Pathology Charité Universitätsmedizin Berlin Germany Department of Artificial Intelligence Korea University Seoul Korea Republic of Max-Planck Institute for Informatics Saarbrücken Germany

Multiple instance learning (MIL) is an effective and widely used approach for weakly supervised machine learning. In histopathology, MIL models have achieved remarkable success in tasks like tumor detection, biomarker prediction, and outcome prognostication. However, MIL explanation methods are still lagging behind, as they are limited to small bag sizes or disregard instance interactions. We revisit MIL through the lens of explainable AI (XAI) and introduce xMIL, a refined framework with more general assumptions. We demonstrate how to obtain improved MIL explanations using layer-wise relevance propagation (LRP) and conduct extensive evaluation experiments on three toy settings and four real-world histopathology datasets. Our approach consistently outperforms previous explanation attempts with particularly improved faithfulness scores on challenging biomarker prediction tasks. Finally, we showcase how xMIL explanations enable pathologists to extract insights from MIL models, representing a significant advance for knowledge discovery and model debugging in digital histopathology. Codes are available at: https://***/bifold-pathomics/xMIL. © 2024 Neural information processing systems foundation. All rights reserved.

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xMIL: insightful explanations for multiple instance learning in histopathology 24

xMIL: insightful explanations for multiple instance learning...

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

作者： Julius Hense Mina Jamshidi Idaji Oliver Eberle Thomas Schnake Jonas Dippel Laure Ciernik Oliver Buchstab Andreas Mock Frederick Klauschen Klaus-Robert Müller Berlin Institute for the Foundations of Learning and Data Berlin Germany and Machine Learning Group Technische Universität Berlin Berlin Germany Berlin Institute for the Foundations of Learning and Data Berlin Germany and Machine Learning Group Technische Universität Berlin Berlin Germany and Aignostics GmbH Berlin Germany Institute of Pathology Ludwig Maximilian University Munich Germany Institute of Pathology Ludwig Maximilian University Munich Germany and German Cancer Research Center Heidelberg and German Cancer Consortium Munich Germany Berlin Institute for the Foundations of Learning and Data Berlin Germany and Institute of Pathology Ludwig Maximilian University Munich Germany and German Cancer Research Center Heidelberg and German Cancer Consortium Munich Germany and Institute of Pathology Charité Universitätsmedizin Berlin Germany Berlin Institute for the Foundations of Learning and Data Berlin Germany and Machine Learning Group Technische Universität Berlin Berlin Germany and Department of Artificial Intelligence Korea University Seoul Korea and Max-Planck Institute for Informatics Saarbrücken Germany

ISBN: (纸本)9798331314385

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Nonequilibrium Universality of Rydberg-Excitation Spreading on a Dynamic Network

arXiv

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arXiv 2025年

作者： Ohler, Simon Brady, Daniel Mischke, Patrick Bender, Jana Ott, Herwig Niederprüm, Thomas Ripken, Winfried Otterbach, Johannes S. Fleischhauer, Michael Department of Physics Research Center OPTIMAS RPTU Kaiserslautern Kaiserslautern67663 Germany Mainz55128 Germany Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Orthogonal Otter UG Berlin10961 Germany

Understanding the universal properties of non-equilibrium phase transitions of spreading processes is a challenging problem. This applies in particular to irregular and dynamically varying networks. We here investigate an experimentally accessible model system for such processes, namely the absorbing-state phase transition (ASPT) of Rydberg-excitation spreading, known as Rydberg facilitation, in a laser-driven gas of mobile atoms. It occurs on an irregular graph, set by the random atom positions in the gas and, depending on temperature, changes its character from static to dynamic. By studying the behavior of the order parameter in [Phys. Rev. Lett. 133, 173401 (2024)] we showed numerical evidence for a crossover from directed percolation (DP) universality through various phases of anomalous directed percolation (ADP) to mean-field (MF) behavior when the temperature of the gas is increased. As the behavior of the order parameter is not sufficient to uniquely determine the universality class, we here analyze the distribution of avalanches – characteristic of non-equilibrium critical behavior – to fully characterize the ASPT. Performing extended numerical calculations and experiments on a cold 87Rb atom gas we confirm our earlier numerical findings and our phenomenological model that maps the dynamic network to a static one with power-law tails of the distribution of excitation distances. Furthermore we discuss the influence of dissipation, present in the experiment and a necessary ingredient for the self-organization of the system to the critical point. In particular we study the potential modification of the universality class by losses as a function of dissipation strength. © 2025, CC BY.

关键词： Rydberg states

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The Conditional Cauchy-Schwarz Divergence With Applications to Time-Series data and Sequential Decision Making

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IEEE Transactions on Pattern Analysis and machine Intelligence 2025年第7期47卷 5901-5917页

作者： Shujian Yu Hongming Li Sigurd Løkse Robert Jenssen José C. Príncipe Machine Learning Group UiT - The Arctic University of Norway Tromsø Norway Quantitative Data Analytics Group Vrije Universiteit Amsterdam Amsterdam The Netherlands Department of Electrical and Computer Engineering University of Florida Gainesville FL USA Drones and Autonomous Systems Group NORCE Norwegian Research Centre Tromsø Norway Pioneer AI Centre Copenhagen University København Denmark Norwegian Computing Center Oslo Norway

The Cauchy-Schwarz (CS) divergence was developed by Príncipe et al. in 2000. In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence can be elegantly estimated by a kernel density estimator from given samples. We illustrate the advantages (e.g., rigorous faithfulness guarantee, lower computational complexity, higher statistical power, and much more flexibility in a wide range of applications) of our conditional CS divergence over previous proposals, such as the conditional Kullback-Leibler divergence and the conditional maximum mean discrepancy. We also demonstrate the compelling performance of conditional CS divergence in two machine learning tasks related to time series data and sequential inference, namely time series clustering and uncertainty-guided exploration for sequential decision making.

关键词： Covariance matrices Kernel machine learning Time series analysis Vectors Symmetric matrices Q measurement Matrix decomposition Estimation Decision making

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So3krates: equivariant attention for interactions on arbitrary length-scales in molecular systems 22

So3krates: equivariant attention for interactions on arbitra...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： J. Thorben Frank Oliver T. Unke Klaus-Robert Müller Machine Learning Group TU Berlin Berlin Germany and BIFOLD Berlin Institute for the Foundations of Learning and Data Germany Machine Learning Group TU Berlin Berlin Germany and BIFOLD Berlin Institute for the Foundations of Learning and Data Germany and Google Research Brain team Berlin Machine Learning Group TU Berlin Berlin Germany and BIFOLD Berlin Institute for the Foundations of Learning and Data Germany and Google Research Brain team Berlin and Department of Artificial Intelligence Korea University Seoul Korea and Max Planck Institut für Informatik Saarbrücken Germany

ISBN: (纸本)9781713871088

The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-initio methods. However, some quantum mechanical properties of molecules and materials depend on non-local electronic effects, which are often neglected due to the difficulty of modeling them efficiently. This work proposes a modified attention mechanism adapted to the underlying physics, which allows to recover the relevant non-local effects. Namely, we introduce spherical harmonic coordinates (SPHCs) to reflect higher-order geometric information for each atom in a molecule, enabling a non-local formulation of attention in the SPHC space. Our proposed model So3KRATES- a self-attention based message passing neural network - uncouples geometric information from atomic features, making them independently amenable to attention mechanisms. Thereby we construct spherical filters, which extend the concept of continuous filters in Euclidean space to SPHC space and serve as foundation for a spherical self-attention mechanism. We show that in contrast to other published methods, So3krates is able to describe nonlocal quantum mechanical effects over arbitrary length scales. Further, we find evidence that the inclusion of higher-order geometric correlations increases data efficiency and improves generalization. So3krates matches or exceeds state- of-the-art performance on popular benchmarks, notably, requiring a significantly lower number of parameters (0.25-0.4x) while at the same time giving a substantial speedup (6-14x for training and 2-11x for inference) compared to other models.

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