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IEEE Workshop on machine learning for Signal Processing

作者： Frederik Hoppe Claudio Mayrink Verdun Hannah Laus Felix Krahmer Holger Rauhut Chair of Mathematics of Information Processing RWTH Aachen University Aachen Germany Department of Mathematics Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Technical University of Munich Munich Germany

Model-based deep learning solutions to inverse problems have attracted increasing attention in recent years as they bridge state-of-the-art numerical performance with interpretability. In addition, the incorporated prior domain knowledge can make the training more efficient as the smaller number of parameters allows the training step to be executed with smaller datasets. Algorithm unrolling schemes stand out among these model-based learning techniques. Despite their rapid advancement and their close connection to traditional high-dimensional statistical methods, they lack certainty estimates and a theory for uncertainty quantification is still elusive. This work provides a step towards closing this gap proposing a rigorous way to obtain confidence intervals for the LISTA estimator.

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The Role of IoT and machine learning in Automating Space Docking: Challenges, Advancements, and Future Prospects 2

The Role of IoT and Machine Learning in Automating Space Doc...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Space docking is an important aspect of modern-day space exploration, enabling crewed missions, shipment transport, and spacecraft refuelling. Traditionally, manual docking has trusted astronaut precision and floor manipulation coordination, which has grown to be inefficient and blunders-susceptible in deep-space missions because of conversation delays. Integrating the Internet of Things (IoT) and machine learning (ML) is reworking area docking by introducing self-reliant, actual-time selection-making structures. IoT-enabled sensors continuously monitor spacecraft role, pace, and orientation, while ML algorithms optimize docking manoeuvres, beautify predictive upkeep, and make certain collision avoidance. Reinforcement learning similarly allows adaptive management strategies, making self-sufficient docking more efficient and dependable. This paper explores the improvements in IoT and ML for automating space docking, reading real-global programs along with SpaceX Dragon, NASA's AR&D systems, and China's Tianzhou cargo deliver. By addressing key demanding situations-precision, pace, and safety-those technologies pave the way for sustainable space exploration, including future Mars missions and autonomous area stations. The observation highlights how AI-driven docking may be integral for deep-space operations, making sure safer and more green missions are made. © 2025 IEEE.

关键词： Reinforcement learning

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The Role of IoT and machine learning in Automating Space Docking: Challenges, Advancements, and Future Prospects

The Role of IoT and Machine Learning in Automating Space Doc...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Nayan Jikar Yash Tale Abhay Tale Aditya Barhate Prateek Verma Aman Jikar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

关键词： Space vehicles Mars Space missions Reinforcement learning Prediction algorithms Real-time systems Sensors Internet of Things Next generation networking Monitoring

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Role of machine learning in Climate Change Prediction and Mitigation 2

Role of Machine Learning in Climate Change Prediction and Mi...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

ISBN: (纸本)9798331505745

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action. © 2025 IEEE.

关键词： Global warming

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Big data in Social Media: Analyzing Trends, Patterns and Challenges

Big Data in Social Media: Analyzing Trends, Patterns and Cha...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The huge volumes of data produced in social media provide both new possibilities and challenges to analytics. The present paper emphasizes Big data analytics and machine learning (ML) methods to uncover trends, patterns, and user behavior from social media-related data. Such vital concepts, like the “Four Vs” of Big data: Volume, Velocity, Variety, and Veracity, and innovative tools for datacollection, storage, and preprocessing are covered here as well. Sentiment analysis, trend forecasting, and user behaviormodeling are some of the examples of what is possible using ML and Artificial Intelligence (AI) for deriving actionable insights. Marketing campaigns, crisis communications, and political campaigns are some of the scenarios that are covered to bring out real world applications of such technologies, along with their transformative effects. Lastly, data privacy, quality, and scalability issues are noted, along with ethical concerns and biases. Directions for the future point to developments in real-time data analysis, anonymization, and the changing role of AI in social media platforms. This research emphasizes the significance of Big data and machine learning in driving innovation and decision-making in the digital era.

关键词： data privacy Technological innovation Ethics Social networking (online) machine learning Big data Market research Real-time systems Information filtering Information integrity

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Exploring the Tangible Impact of Artificial Intelligence and machine learning: Bridging the Gap between Hype and Reality

Exploring the Tangible Impact of Artificial Intelligence and...

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Advanced Computing and Emerging Technologies (ACET), International Conference on

作者： Rahul Dattangire Ruchika Vaidya Divya Biradar Ashish Joon Data Engineering Publicis Sapient Houston Texas USA Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Computer Science University of Texas at Arlington Arlington Texas USA Software Developer Andhus Technologies Inc Pensacola FL

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

Beyond being merely trendy terms in technology, machine learning (ML) and artificial intelligence (AI) have directly influenced every industry. This article thus demystifies the true meaning of these technologies by sifting through the sultry descriptions. Here, we explore how AI and ML enhance efficiency, decision-making, and problem-solving in various sectors including health, Agriculture, finance, and production as we discover real-world applications. Continuing with the discussion of implementation challenges, the aspects of bias, ethical pitfalls, and report cards are examined. In addition, the article also covered how the integration of AI as well as ML remains a way of improving processes, creating better user experiences, and spurring innovation. This paper, which is also based on case studies, discusses the relationship between social welfare and AI and the importance of AI’s development and use being done responsibly. Considering all the discussed aspects, this volume provides a rather nuanced perspective on AI and ML, unmasking the myths around these concepts to reveal the fact that they have a highly significant impact on the current reality and its projected evolutions.

关键词： Technological innovation Ethics Decision making Finance machine learning Production Pressing Medical services Writing Problem-solving

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CONSTRAINED CONSENSUS-BASED OPTIMIZATION AND NUMERICAL HEURISTICS FOR THE FEW PARTICLE REGIME

arXiv

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arXiv 2024年

作者： Beddrich, Jonas Chenchene, Enis Fornasier, Massimo Huang, Hui Wohlmuth, Barbara Faculty of Mathematics University of Vienna Austria Department of Mathematics Munich Data Science Institute Technical University of Munich Garching by Munich & Munich Center for Machine Learning Munich Germany Department of Mathematics and Scientific Computing University of Graz Austria Department of Mathematics Technical University of Munich Garching by Munich Germany

Consensus-based optimization (CBO) is a versatile multi-particle optimization method for performing nonconvex and nonsmooth global optimizations in high dimensions. Proofs of global convergence in probability have been achieved for a broad class of objective functions in unconstrained optimizations. In this work we adapt the algorithm for solving constrained optimizations on compact and unbounded domains with boundary by leveraging emerging reflective boundary conditions. In particular, we close a relevant gap in the literature by providing a global convergence proof for the many-particle regime comprehensive of convergence rates. On the one hand, for the sake of minimizing running cost, it is desirable to keep the number of particles small. On the other hand, reducing the number of particles implies a diminished capability of exploration of the algorithm. Hence numerical heuristics are needed to ensure convergence of CBO in the few-particle regime. In this work, we also significantly improve the convergence and complexity of CBO by utilizing an adaptive region control mechanism and by choosing geometry-specific random noise. In particular, by combining a hierarchical noise structure with a multigrid finite element method, we are able to compute global minimizers for a constrained p-Allen–Cahn problem with obstacles, a very challenging variational *** Codes 90C26, 90C56, 90C59, 65K10, 35Q90, 60H10 © 2024, CC BY.

关键词： Constrained optimization

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VICE: variational interpretable concept embeddings 22

VICE: variational interpretable concept embeddings

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

作者： Lukas Muttenthaler Charles Y. Zheng Patrick McClure Robert A. Vandermeulen Martin N. Hebart Francisco Pereira Machine Learning Group Technische Universität Berlin Berlin Institute for the Foundations of Learning and Data (BIFOLD) Berlin Germany Machine Learning Team FMRI Facility National Institute of Mental Health Bethesda MD Department of Computer Science Naval Postgraduate School Monterey CA Vision and Computational Cognition Group MPI for Human Cognitive and Brain Sciences Leipzig Germany

ISBN: (纸本)9781713871088

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the triplet odd-one-out task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations, highlighting the unique advantage of using VICE for deriving interpretable embeddings from human behavior.

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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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Consensus-Based Optimization with Truncated Noise

arXiv

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arXiv 2023年

作者： Fornasier, Massimo Richtárik, Peter Riedl, Konstantin Sun, Lukang Technical University of Munich School of Computation Information and Technology Department of Mathematics Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Germany King Abdullah University of Science and Technology Thuwal Saudi Arabia KAUST AI Initiative Thuwal Saudi Arabia SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Thuwal Saudi Arabia

Consensus-based optimization (CBO) is a versatile multi-particle metaheuristic optimization method suitable for performing nonconvex and nonsmooth global optimizations in high dimensions. It has proven effective in various applications while at the same time being amenable to a theoretical convergence analysis. In this paper, we explore a variant of CBO, which incorporates truncated noise in order to enhance the well-behavedness of the statistics of the law of the dynamics. By introducing this additional truncation in the noise term of the CBO dynamics, we achieve that, in contrast to the original version, higher moments of the law of the particle system can be effectively bounded. As a result, our proposed variant exhibits enhanced convergence performance, allowing in particular for wider flexibility in choosing the noise parameter of the method as we confirm experimentally. By analyzing the time-evolution of the Wasserstein-2 distance between the empirical measure of the interacting particle system and the global minimizer of the objective function, we rigorously prove convergence in expectation of the proposed CBO variant requiring only minimal assumptions on the objective function and on the initialization. Numerical evidences demonstrate the benefit of truncating the noise in CBO. © 2023, CC BY.

关键词： Global optimization

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