26,027 results for machine learning

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   Machine learning modularity

   Fan, Yi; Jejjala, Vishnu; Lei, Yang

   arXiv · 2026 · English · arXiv

   Based on a transformer based sequence-to-sequence architecture combined with a dynamic batching algorithm, this work introduces a machine learning framework for automatically simplifying complex expressions involving multiple elliptic Gamma functions, including the $q$-$θ$ function and the elliptic…

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   Machine Learning in Epidemiology

   Wright, Marvin N.; Burk, Lukas; Golchian, Pegah et al.

   arXiv · 2026 · English · arXiv

   In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools that can help to analyze such enormous amounts of data. This chapter lays the methodological foundations for…

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   Machine Learning H-theorem

   Lier, Ruben

   arXiv · 2025 · English · arXiv

   H-theorem provides a microscopic foundation of the Second Law of Thermodynamics and is therefore essential to establishing statistical physics, but at the same time, H-theorem has been subject to controversy that in part persists till this day. To better understand H-theorem and its relation to the…

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   Machine Learning Invariants of Tensors

   Elamaran, Athithan; Ferko, Christian; Scarlett, Sterling

   arXiv · 2025 · English · arXiv

   We propose a data-driven approach to identifying the functionally independent invariants that can be constructed from a tensor with a given symmetry structure. Our algorithm proceeds by first enumerating graphs, or tensor networks, that represent inequivalent contractions of a product of tensors…

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   Machine learning for hydrodynamic stability

   Silvester, David J.

   arXiv · 2024 · English · arXiv

   A machine-learning strategy for investigating the stability of fluid flow problems is proposed herein. The goal is to provide a simple yet robust methodology to find a nonlinear mapping from the parametric space to an indicator representing the probability of observing a bifurcated solution. The…

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   Combinatorial Optimization Augmented Machine Learning

   Schiffer, Maximilian; Hoppe, Heiko; Su, Yue et al.

   arXiv · 2026 · English · arXiv

   Combinatorial optimization augmented machine learning (COAML) has recently emerged as a powerful paradigm for integrating predictive models with combinatorial decision-making. By embedding combinatorial optimization oracles into learning pipelines, COAML enables the construction of policies that…

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   The Rhetoric of Machine Learning

   Williamson, Robert C.

   arXiv · 2026 · English · arXiv

   I examine the technology of machine learning from the perspective of rhetoric, which is simply the art of persuasion. Rather than being a neutral and "objective" way to build "world models" from data, machine learning is (I argue) inherently rhetorical. I explore some of its rhetorical features…

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   LakeMLB: Data Lake Machine Learning Benchmark

   Pan, Feiyu; Zhang, Tianbin; Zhang, Aoqian et al.

   arXiv · 2026 · English · arXiv

   Modern data lakes have emerged as foundational platforms for large-scale machine learning, enabling flexible storage of heterogeneous data and structured analytics through table-oriented abstractions. Despite their growing importance, standardized benchmarks for evaluating machine learning…

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   SoK: Data Minimization in Machine Learning

   Staab, Robin; Jovanović, Nikola; Mai, Kimberly et al.

   arXiv · 2025 · English · arXiv

   …Notably, the relevance of data minimization is particularly pronounced in machine learning (ML) applications, which typically rely on large datasets, resulting in an emerging research area known as Data Minimization in Machine Learning (DMML). At the same time, existing work on other ML privacy and…

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    Quality Model for Machine Learning Components

    Lewis, Grace A.; Brower-Sinning, Rachel; Edman, Robert et al.

    arXiv · 2026 · English · arXiv

    Despite increased adoption and advances in machine learning (ML), there are studies showing that many ML prototypes do not reach the production stage and that testing is still largely limited to testing model properties, such as model performance, without considering requirements derived from the…

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    Predicting galaxy bias using machine learning

    Riveros-Jara, Catalina; Montero-Dorta, Antonio D.; Rodrigues, Natália V. N. et al.

    arXiv · 2026 · English · arXiv

    …described through the galaxy bias, $b$, can be studied effectively using machine learning (ML) techniques, which offer strong predictive capabilities and can capture non-linear relationships. We aim to incorporate the linear bias parameter assigned to individual galaxies into a ML framework…

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    Gradient-Informed Machine Learning in Electromagnetics

    Zorzetto, Matteo; Backmeyer, Merle; Wiesheu, Michael et al.

    arXiv · 2026 · English · arXiv

    …model of a parametric nonlinear model of a permanent magnet synchronous machine. The differentiable nature of IGA allows for computationally efficient extraction of parametric sensitivities, which are leveraged for gradient-enhanced surrogate modeling.

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    Query Languages for Machine-Learning Models

    Grohe, Martin

    arXiv · 2026 · English · arXiv

    …Van den Bussche, we have investigated these logics as query languages for machine learning models, specifically neural networks, which are naturally represented as weighted graphs. I present illustrative examples of queries to neural networks that can be expressed in these logics and discuss…

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    Curating art exhibitions using machine learning

    Covas, Eurico

    arXiv · 2025 · English · arXiv

    Here we present a series of artificial models - a total of four related models - based on machine learning techniques that attempt to learn from existing exhibitions which have been curated by human experts, in order to be able to do similar curatorship work. Out of our four artificial intelligence…

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    Quantum Machine Learning for Climate Modelling

    Schwabe, Mierk; Pastori, Lorenzo; Sarandrea, Valentina et al.

    arXiv · 2025 · English · arXiv

    Quantum machine learning (QML) is making rapid progress, and QML-based models hold the promise of quantum advantages such as potentially higher expressivity and generalizability than their classical counterparts. Here, we present work on using a quantum neural net (QNN) to develop a…

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    Algorithms with Calibrated Machine Learning Predictions

    Shen, Judy Hanwen; Vitercik, Ellen; Wikum, Anders

    arXiv · 2025 · English · arXiv

    The field of algorithms with predictions incorporates machine learning advice in the design of online algorithms to improve real-world performance. A central consideration is the extent to which predictions can be trusted -- while existing approaches often require users to specify an aggregate…

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    Machine Learning Transferability for Malware Detection

    Vieira, César; Vitorino, João; Maia, Eva et al.

    arXiv · 2026 · English · arXiv

    …to evade detection. Despite the ongoing efforts in the development of Machine Learning (ML) detection approaches, there is still a lack of feature compatibility in public datasets. This limits generalization when facing distribution shifts, as well as transferability to different datasets. This…

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    Reduced Density Matrices Through Machine Learning

    Azam, Awwab A.; Zhao, Lexu; Yu, Jiabin

    arXiv · 2025 · English · arXiv

    $n$-particle reduced density matrices ($n$-RDMs) play a central role in understanding correlated phases of matter, but their calculation is often computationally inefficient for strongly-correlated states at large system sizes. In this work, we use neural network (NN) architectures to accelerate…

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    Double Machine Learning for Time Series

    Ciganovic, Milos; D'Amario, Federico; Tancioni, Massimiliano

    arXiv · 2026 · English · arXiv

    We modify the Double Machine Learning estimator to broaden its applicability to macroeconomic time-series settings. A deterministic cross-fitting step, termed Reverse Cross-Fitting, leverages the time-reversibility of stationary series to improve sample utilization and efficiency. We detail and…

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    Cluster Attention for Graph Machine Learning

    Platonov, Oleg; Prokhorenkova, Liudmila

    arXiv · 2026 · English · arXiv

    Message Passing Neural Networks have recently become the most popular approach to graph machine learning tasks; however, their receptive field is limited by the number of message passing layers. To increase the receptive field, Graph Transformers with global attention have been proposed; however…

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