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arXiv 2021年

作者： März, Luisa Asgari, Ehsaneddin Braune, Fabienne Zimmermann, Franziska Roth, Benjamin Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria Nlp Expert Center Data:Lab Volkswagen Ag Munich Germany

The absence of labeled data for training neural models is often addressed by leveraging knowledge about the specific task, resulting in heuristic but noisy labels. The knowledge is captured in labeling functions, which detect certain regularities or patterns in the training samples and annotate corresponding labels for training. This process of weakly supervised training may result in an over-reliance on the signals captured by the labeling functions and hinder models to exploit other signals or to generalize well. We propose KnowMAN, an adversarial scheme that enables to control influence of signals associated with specific labeling functions. KnowMAN forces the network to learn representations that are invariant to those signals and to pick up other signals that are more generally associated with an output label. KnowMAN strongly improves results compared to direct weakly supervised learning with a pre-trained transformer language model and a feature-based baseline. © 2021, CC BY.

关键词： Computational linguistics

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: Quantum Artificial Intelligence for the Automotive Industry

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KI - Künstliche Intelligenz 2024年第4期38卷 351-359页

作者： Stollenwerk, Tobias Bhattacharya, Somtapa Cattelan, Michele Ciani, Alessandro Compostella, Gabriele Headley, David Klepsch, Johannes Klusch, Matthias Leder, Markus Macaluso, Antonio Michielsen, Kristel Nabok, Dmytro Papanikolaou, Anestis Rausch, Alexander Schumann, Marco Skolik, Andrea Yarkoni, Sheir Wilhelm, Frank K. Institute for Quantum Computing Analytics (PGI-12) Forschungszentrum Jülich Jülich Germany Volkswagen Data:Lab Volkswagen Group Munich Germany Machine Learning Research Lab Volkswagen Group Munich Germany Mercedes-Benz AG Stuttgart Germany BMW AG München Germany German Research Center for Artificial Intelligence (DFKI) Saarbrüecken Germany Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich Jülich Germany Robert Bosch GmbH Corporate Research Renningen Germany

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Flow-Based Sampling for Entanglement Entropy and the machine learning of Defects

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Physical Review Letters 2025年第15期134卷 151601-151601页

作者： Andrea Bulgarelli Elia Cellini Karl Jansen Stefan Kühn Alessandro Nada Shinichi Nakajima Kim A. Nicoli Marco Panero Department of Physics University of Turin and INFN Turin unit Via Pietro Giuria 1 I-10125 Turin Italy Computation-Based Science and Technology Research Center The Cyprus Institute Nicosia Cyprus Deutsches Elektronen-Synchrotron DESY Zeuthen Germany Berlin Institute for the Foundations of Learning and Data (BIFOLD) Berlin Germany Machine Learning Group Technische Universität Berlin Berlin Germany RIKEN Center for AIP Tokyo Japan Transdisciplinary Research Area (TRA) Matter University of Bonn Germany Helmholtz Institute for Radiation and Nuclear Physics (HISKP) Bonn Germany Department of Physics and Helsinki Institute of Physics PL 64 FIN-00014 University of Helsinki Finland

We introduce a novel technique to numerically calculate Rényi entanglement entropies in lattice quantum field theory using generative models. We describe how flow-based approaches can be combined with the replica trick using a custom neural-network architecture around a lattice defect connecting two replicas. Numerical tests for the ϕ4 scalar field theory in two and three dimensions demonstrate that our technique outperforms state-of-the-art Monte Carlo calculations, and exhibit a promising scaling with the defect size.

关键词： Lattice theory

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S$^\text{3}$Attention: Improving Long Sequence Attention With Smoothed Skeleton Sketching

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IEEE Journal of Selected Topics in Signal Processing 2024年第6期18卷 985-996页

作者： Xue Wang Tian Zhou Jianqing Zhu Jialin Liu Kun Yuan Tao Yao Wotao Yin Rong Jin HanQin Cai Alibaba Group Bellevue WA USA Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Statistics and Data Science University of Central Florida Orlando FL USA Center for Machine Learning Research Peking University Beijing China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai China Meta Menlo Park CA USA Department of Statistics and Data Science and the Department of Computer Science University of Central Florida Orlando FL USA

Attention based models have achieved many remarkable breakthroughs in numerous applications. However, the quadratic complexity of Attention makes the vanilla Attention based models hard to apply to long sequence tasks. Various improved Attention structures are proposed to reduce the computation cost by inducing low rankness and approximating the whole sequence by sub-sequences. The most challenging part of those approaches is maintaining the proper balance between information preservation and computation reduction: the longer sub-sequences used, the better information is preserved, but at the price of introducing more noise and computational costs. In this paper, we propose a smoothed skeleton sketching based Attention structure, coined S $^{3}$ Attention, which significantly improves upon the previous attempts to negotiate this trade-off. S $^{3}$ Attention has two mechanisms to effectively minimize the impact of noise while keeping the linear complexity to the sequence length: a smoothing block to mix information over long sequences and a matrix sketching method that simultaneously selects columns and rows from the input matrix. We verify the effectiveness of S $^{3}$ Attention both theoretically and empirically. Extensive studies over Long Range Arena (LRA) datasets and six time-series forecasting show that S $^{3}$ Attention significantly outperforms both vanilla Attention and other state-of-the-art variants of Attention structures.

关键词： Computational modeling Complexity theory Transformers Task analysis Skeleton Kernel Noise

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Beyond Gaussian Noise: A Generalized Approach to Likelihood analysis with non-Gaussian Noise

arXiv

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arXiv 2023年

作者： Legin, Ronan Adam, Alexandre Hezaveh, Yashar Levasseur, Laurence Perreault Department of Physics Université de Montréal Montréal Canada Ciela Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Mila Quebec Artificial Intelligence Institute Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States

Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function of complex, high-dimensional, non-Gaussian, and anisotropic noise. This is a major limitation for precision measurements in many domains of science, including astrophysics, for example, for the analysis of the Cosmic Microwave Background, gravitational waves, gravitational lensing, and exoplanets. This work presents Score-based LIkelihood Characterization (SLIC), a framework that resolves this issue by building a data-driven noise model using a set of noise realizations from observations. We show that the approach produces unbiased and precise likelihoods even in the presence of highly non-Gaussian correlated and spatially varying noise. We use diffusion generative models to estimate the gradient of the probability density of noise with respect to data elements. In combination with the Jacobian of the physical model of the signal, we use Langevin sampling to produce independent samples from the unbiased likelihood. We demonstrate the effectiveness of the method using real data from the Hubble Space Telescope and James Webb Space Telescope. © 2023, CC BY.

关键词： Gaussian distribution

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DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids

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

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Iosif Tsangko Andreas Triantafyllopoulos Michael Müller Hendrik Schröter Björn W. Schuller EIHW – Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany CHI – Chair of Health Informatics 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 UK MDSI – Munich Data Science Institute Munich Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： Performance evaluation Adaptation models Computational modeling Noise Noise reduction Computer architecture Speech enhancement Fingerprint recognition Hearing aids Recording

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EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulation

arXiv

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arXiv 2023年

作者： Zhao, Yue Zhang, Wei Li, Tiejun Center for Data Science Peking University Beijing100871 China Zuse Institute Berlin Berlin14195 Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin14195 Germany LMAM and School of Mathematical Sciences Peking University Beijing100871 China Center for Machine Learning Research Peking University Beijing100871 China

We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state (NESS) systems. EPR-Net leverages a nice mathematical fact that the desired negative potential gradient is simply the orthogonal projection of the driving force of the underlying dynamics in a weighted inner-product space. Remarkably, our loss function has an intimate connection with the steady entropy production rate (EPR), enabling simultaneous landscape construction and EPR estimation. We introduce an enhanced learning strategy for systems with small noise, and extend our framework to include dimensionality reduction and state-dependent diffusion coefficient case in a unified fashion. Comparative evaluations on benchmark problems demonstrate the superior accuracy, effectiveness, and robustness of EPR-Net compared to existing methods. We apply our approach to challenging biophysical problems, such as an 8D limit cycle and a 52D multi-stability problem, which provide accurate solutions and interesting insights on constructed landscapes. With its versatility and power, EPR-Net offers a promising solution for diverse landscape construction problems in biophysics. Copyright © 2023, The Authors. All rights reserved.

关键词： Entropy

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Object Segmentation Tracking from Generic Video Cues

Object Segmentation Tracking from Generic Video Cues

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International Conference on Pattern Recognition

作者： Amirhossein Kardoost Sabine Müller Joachim Weickert Margret Keuper Data and Web Science Group University of Mannheim Mannheim Germany Fraunhofer Center Machine Learning Germany Mathematical Image Analysis Group Saarland University Saarbrücken Germany

We propose a light-weight variational framework for online tracking of object segmentations in videos based on optical flow and image boundaries. While high-end computer vision methods on this task rely on sequence specific training of dedicated CNN architectures, we show the potential of a variational model, based on generic video information from motion and color. Such cues are usually required for tasks such as robot navigation or grasp estimation. We leverage them directly for video object segmentation and thus provide accurate segmentations at potentially very low extra cost. Our simple method can provide competitive results compared to the costly CNN-based methods with parameter tuning. Furthermore, we show that our approach can be combined with state-of-the-art CNN-based segmentations in order to improve over their respective results. We evaluate our method on the datasets DAVIS16,17 and SegTrack v2.

关键词： Training Navigation Annotations Video sequences Estimation Object segmentation Task analysis

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analysis of the research Status of Information Technology Education Literature in China and Abroad 24

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

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Yulin Liu, Kai Li, Junke School of Information Engineering Suqian University Jiangsu Suqian223800 China School of Computer and Information Qiannan Normal University for Nationalities Duyun558000 China Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment Jiangsu 223800 China Key Laboratory of Machine Learning and Unstructured Data Processing of Qiannan Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun558000 China

ISBN: (纸本)9798350319934

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. © 2022 IEEE.

关键词： Informatization

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DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids

arXiv

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arXiv 2025年

作者： Tsangko, Iosif Triantafyllopoulos, Andreas Müller, Michael Schröter, Hendrik Schuller, Björn W. EIHW – Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany CHI – Chair of Health Informatics 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

关键词： Budget control

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