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arXiv 2024年

作者： Morik, Marco Hashemi, Ali Müller, Klaus-Robert Haufe, Stefan Nakajima, Shinichi The Berlin Institute for the Foundations of Learning and Data Berlin Germany Machine Learning Group Department of Computer Science TU Berlin Berlin Germany Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of Max Planck Institut für Informatik Saarbrücken Germany Bernstein Center for Computational Neuroscience Berlin Germany Physikalisch-Technische Bundesanstalt Berlin Germany Berlin Center for Advanced Neuroimaging Charité-Universitätsmedizin Berlin Germany RIKEN AIP Tokyo Japan

Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high temporal resolution. However, identifying the correct spatial location of brain sources from these signals remains difficult due to the ill-posed structure of the problem. Traditional methods predominantly rely on manually crafted priors, missing the flexibility of data-driven learning, while recent deep learning approaches focus on end-to-end learning, typically using the physical information of the forward model only for generating training data. We propose the novel hybrid method 3D-PIUNet for EEG source localization that effectively integrates the strengths of traditional and deep learning techniques. 3D-PIUNet starts from an initial physics-informed estimate by using the pseudo inverse to map from measurements to source space. Secondly, by viewing the brain as a 3D volume, we use a 3D convolutional U-Net to capture spatial dependencies and refine the solution according to the learned data prior. Training the model relies on simulated pseudo-realistic brain source data, covering different source distributions. Trained on this data, our model significantly improves spatial accuracy, demonstrating superior performance over both traditional and end-to-end data-driven methods. Additionally, we validate our findings with real EEG data from a visual task, where 3D-PIUNet successfully identifies the visual cortex and reconstructs the expected temporal behavior, thereby showcasing its practical applicability. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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GAIA: A Global, Multi-modal, Multi-scale Vision-Language dataset for Remote Sensing Image Analysis

arXiv

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arXiv 2025年

作者： Zavras, Angelos Michail, Dimitrios Zhu, Xiao Xiang Demir, Begüm Papoutsis, Ioannis Orion Lab School of Rural Surveying and Geoinformatics Engineering National Technical University of Athens Athens15772 Greece Institute of Astronomy Astrophysics Space Applications and Remote Sensing National Observatory of Athens Athens11810 Greece Department of Informatics and Telematics Harokopio University of Athens Athens17676 Greece Chair of Data Science in Earth Observation Technical University of Munich Munich80333 Germany Munich Center for Machine Learning Munich80333 Germany Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Berlin10623 Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin10623 Germany

The continuous operation of Earth-orbiting satellites generates vast and ever-growing archives of Remote Sensing (RS) images. Natural language presents an intuitive interface for accessing, querying, and interpreting the data from such archives. However, existing Vision-Language Models (VLMs) are predominantly trained on web-scraped, noisy image-text data, exhibiting limited exposure to the specialized domain of RS. This deficiency results in poor performance on RS-specific tasks, as commonly used datasets often lack detailed, scientifically accurate textual descriptions and instead emphasize solely on attributes like date and location. To bridge this critical gap, we introduce GAIA, a novel dataset designed for multi-scale, multi-sensor, and multi-modal RS image analysis. GAIA comprises of 205,150 meticulously curated RS image-text pairs, representing a diverse range of RS modalities associated to different spatial resolutions. Unlike existing vision-language datasets in RS, GAIA specifically focuses on capturing a diverse range of RS applications, providing unique information about environmental changes, natural disasters, and various other dynamic phenomena. The dataset provides a spatially and temporally balanced distribution, spanning across the globe, covering the last 25 years with a balanced temporal distribution of observations. GAIA’s construction involved a two-stage process: (1) targeted web-scraping of images and accompanying text from reputable RS-related sources, and (2) generation of five high-quality, scientifically grounded synthetic captions for each image using carefully crafted prompts that leverage the advanced vision-language capabilities of GPT-4o. We also release an automated processing framework developed for this purpose, enabling the broader research community to generate captions for RS images using the web-crawled image-text data. Our extensive experiments, including fine-tuning of CLIP and BLIP2 models, demonstrate that GAIA significan

关键词： Visual languages

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Topologically Faithful Multi-class Segmentation in Medical Images

arXiv

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arXiv 2024年

作者： Berger, Alexander H. Lux, Laurin Stucki, Nico Bürgin, Vincent Shit, Suprosanna Banaszak, Anna Rueckert, Daniel Bauer, Ulrich Paetzold, Johannes C. School of Computation Information and Technology Technical University of Munich Munich Germany Munich Data Science Institute Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany School of Medicine and Health Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Computing Imperial College London London United Kingdom Department of Quantitative Biomedicine University of Zurich Switzerland

Topological accuracy in medical image segmentation is a highly important property for downstream applications such as network analysis and flow modeling in vessels or cell counting. Recently, significant methodological advancements have brought well-founded concepts from algebraic topology to binary segmentation. However, these approaches have been underexplored in multi-class segmentation scenarios, where topological errors are common. We propose a general loss function for topologically faithful multi-class segmentation extending the recent Betti matching concept, which is based on induced matchings of persistence barcodes. We project the N-class segmentation problem to N single-class segmentation tasks, which allows us to use 1-parameter persistent homology, making training of neural networks computationally feasible. We validate our method on a comprehensive set of four medical datasets with highly variant topological characteristics. Our loss formulation significantly enhances topological correctness in cardiac, cell, artery-vein, and Circle of Willis segmentation.7 © 2024, CC BY-NC-SA.

关键词： Image segmentation

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Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023

arXiv

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arXiv 2023年

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin García, David López Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Health Data Analytics Program Centre for Applied Artificial Intelligence Macquarie University Sydney2109 Australia Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia Monash University Melbourne Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Department of Psychiatry University of Cambridge United Kingdom

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. © 2023,

关键词： Electroencephalography

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Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics

arXiv

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arXiv 2024年

作者： Chen, Yenho Mudrik, Noga Johnsen, Kyle A. Alagapan, Sankaraleengam Charles, Adam S. Rozell, Christopher J. Machine Learning Center Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Coulter Dept. of Biomedical Engineering Emory University Georgia Institute of Technology United States Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University United States

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model’s ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. © 2024, CC BY-SA.

关键词： State space methods

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Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks

arXiv

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arXiv 2023年

作者： Linhardt, Lorenz Müller, Klaus-Robert Montavon, Grégoire Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data - BIFOLD Berlin10587 Germany Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of Max Planck Institute for Informatics Saarbrücken66123 Germany Google DeepMind Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin14195 Germany

Robustness has become an important consideration in deep learning. With the help of explainable AI, mismatches between an explained model's decision strategy and the user's domain knowledge (e.g. Clever Hans effects) have been identified as a starting point for improving faulty models. However, it is less clear what to do when the user and the explanation agree. In this paper, we demonstrate that acceptance of explanations by the user is not a guarantee for a machine learning model to be robust against Clever Hans effects, which may remain undetected. Such hidden flaws of the model can nevertheless be mitigated, and we demonstrate this by contributing a new method, Explanation-Guided Exposure Minimization (EGEM), that preemptively prunes variations in the ML model that have not been the subject of positive explanation feedback. Experiments demonstrate that our approach leads to models that strongly reduce their reliance on hidden Clever Hans strategies, and consequently achieve higher accuracy on new data. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node data: Whole City Traffic and ETA from Stationary Vehicle Detectors 36

Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph...

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36th Annual Conference on Neural Information Processing Systems - Competition Track, NeurIPS 2022

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab. China Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023 M. Neun et al.

关键词： Travel time

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Reinforcement learning Algorithm for Mixed Mean Field Control Games

arXiv

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arXiv 2022年

作者： Angiuli, Andrea Detering, Nils Fouque, Jean-Pierre Laurière, Mathieu Lin, Jimin Prime Machine Learning Team Amazon. 320 Westlake Ave N SEA83 SeattleWA98109 United States Department of Statistics and Applied Probability South Hall University of California Santa BarbaraCA93106 United States Mathematics and Data Science NYU Shanghai China

We present a new combined mean field control game (MFCG) problem which can be interpreted as a competitive game between collaborating groups and its solution as a Nash equilibrium between groups. Players coordinate their strategies within each group. An example is a modification of the classical trader's problem. Groups of traders maximize their wealth. They face cost for their transactions, for their own terminal positions, and for the average holding within their group. The asset price is impacted by the trades of all agents. We propose a three-timescale reinforcement learning algorithm to approximate the solution of such MFCG problems. We test the algorithm on benchmark linear-quadratic specifications for which we provide analytic *** Codes 91A16, 68Q32 © 2022, CC BY.

关键词： Commerce

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Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows

arXiv

引用

arXiv 2022年

作者： Vaitl, Lorenz Nicoli, Kim Andrea Nakajima, Shinichi Kessel, Pan Machine Learning Group Department of Electrical Engineering & Computer Science Technische Universität Berlin Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Technische Universität Berlin Berlin Germany RIKEN Center for AIP Chuo City Tokyo103-0027 Japan

We propose an algorithm to estimate the path-gradient of both the reverse and forward Kullback–Leibler divergence for an arbitrary manifestly invertible normalizing flow. The resulting path-gradient estimators are straightforward to implement, have lower variance, and lead not only to faster convergence of training but also to better overall approximation results compared to standard total gradient estimators. We also demonstrate that path-gradient training is less susceptible to mode-collapse. In light of our results, we expect that path-gradient estimators will become the new standard method to train normalizing flows for variational inference. © 2022, CC BY.

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Path-Gradient Estimators for Continuous Normalizing Flows

arXiv

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arXiv 2022年

Recent work has established a path-gradient estimator for simple variational Gaussian distributions and has argued that the path-gradient is particularly beneficial in the regime in which the variational distribution approaches the exact target distribution. In many applications, this regime can however not be reached by a simple Gaussian variational distribution. In this work, we overcome this crucial limitation by proposing a path-gradient estimator for the considerably more expressive variational family of continuous normalizing flows. We outline an efficient algorithm to calculate this estimator and establish its superior performance empirically. © 2022, CC BY.

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