Tootfinder

Some thoughts from recent talks: 'AI and Machine Learning in Libraries: Promising, But Not Ready Yet' https://www.openobjects.org.uk/2025/10/ai-and-machine-learning-in-libraries-promising-but-not-ready-yet/

Learning functions, operators and dynamical systems with kernels
Lorenzo Rosasco
https://arxiv.org/abs/2509.18071 https://arxiv.org/pdf/2509.18071

Learning functions, operators and dynamical systems with kernels
This expository article presents the approach to statistical machine learning based on reproducing kernel Hilbert spaces. The basic framework is introduced for scalar-valued learning and then extended to operator learning. Finally, learning dynamical systems is formulated as a suitable operator learning problem, leveraging Koopman operator theory.

Can multimodal representation learning by alignment preserve modality-specific information?
Romain Thoreau, Jessie Levillain, Dawa Derksen
https://arxiv.org/abs/2509.17943 https…

Can multimodal representation learning by alignment preserve modality-specific information?
Combining multimodal data is a key issue in a wide range of machine learning tasks, including many remote sensing problems. In Earth observation, early multimodal data fusion methods were based on specific neural network architectures and supervised learning. Ever since, the scarcity of labeled data has motivated self-supervised learning techniques. State-of-the-art multimodal representation learning techniques leverage the spatial alignment between satellite data from different modalities acqu…

Data Virtualization for Machine Learning
Saiful Khan, Joyraj Chakraborty, Philip Beaucamp, Niraj Bhujel, Min Chen
https://arxiv.org/abs/2507.17293 https://…

Data Virtualization for Machine Learning
Nowadays, machine learning (ML) teams have multiple concurrent ML workflows for different applications. Each workflow typically involves many experiments, iterations, and collaborative activities and commonly takes months and sometimes years from initial data wrangling to model deployment. Organizationally, there is a large amount of intermediate data to be stored, processed, and maintained. \emph{Data virtualization} becomes a critical technology in an infrastructure to serve ML workflows. In …

Machine learning approach to single-shot multiparameter estimation for the non-linear Schr\"odinger equation
Louis Rossignol, Tangui Aladjidi, Myrann Baker-Rasooli, Quentin Glorieux
https://arxiv.org/abs/2509.18479

Machine learning approach to single-shot multiparameter estimation for the non-linear Schrödinger equation
The nonlinear Schrödinger equation (NLSE) is a fundamental model for wave dynamics in nonlinear media ranging from optical fibers to Bose-Einstein condensates. Accurately estimating its parameters, which are often strongly correlated, from a single measurement remains a significant challenge. We address this problem by treating parameter estimation as an inverse problem and training a neural network to invert the NLSE mapping. We combine a fast numerical solver with a machine learning approach…

Functional effects models: Accounting for preference heterogeneity in panel data with machine learning
Nicolas Salvad\'e, Tim Hillel
https://arxiv.org/abs/2509.18047 https:/…

Functional effects models: Accounting for preference heterogeneity in panel data with machine learning
In this paper, we present a general specification for Functional Effects Models, which use Machine Learning (ML) methodologies to learn individual-specific preference parameters from socio-demographic characteristics, therefore accounting for inter-individual heterogeneity in panel choice data. We identify three specific advantages of the Functional Effects Model over traditional fixed, and random/mixed effects models: (i) by mapping individual-specific effects as a function of socio-demographi…

Federated Learning in the Wild: A Comparative Study for Cybersecurity under Non-IID and Unbalanced Settings
Roberto Doriguzzi-Corin, Petr Sabel, Silvio Cretti, Silvio Ranise
https://arxiv.org/abs/2509.17836

Federated Learning in the Wild: A Comparative Study for Cybersecurity under Non-IID and Unbalanced Settings
Machine Learning (ML) techniques have shown strong potential for network traffic analysis; however, their effectiveness depends on access to representative, up-to-date datasets, which is limited in cybersecurity due to privacy and data-sharing restrictions. To address this challenge, Federated Learning (FL) has recently emerged as a novel paradigm that enables collaborative training of ML models across multiple clients while ensuring that sensitive data remains local. Nevertheless, Federated Av…

Toward Affordable and Non-Invasive Detection of Hypoglycemia: A Machine Learning Approach
Lawrence Obiuwevwi, Krzysztof J. Rechowicz, Vikas Ashok, Sampath Jayarathna
https://arxiv.org/abs/2509.17842

Toward Affordable and Non-Invasive Detection of Hypoglycemia: A Machine Learning Approach
Diabetes mellitus is a growing global health issue, with Type 1 Diabetes (T1D) requiring constant monitoring to avoid hypoglycemia. Although Continuous Glucose Monitors (CGMs) are effective, their cost and invasiveness limit access, particularly in low-resource settings. This paper proposes a non-invasive method to classify glycemic states using Galvanic Skin Response (GSR), a biosignal commonly captured by wearable sensors. We use the merged OhioT1DM 2018 and 2020 datasets to build a machine l…

Machine Learning for Campus Energy Resilience: Clustering and Time-Series Forecasting in Intelligent Load Shedding
Salim Oyinlola, Peter Olabisi Oluseyi
https://arxiv.org/abs/2509.17097

Machine Learning for Campus Energy Resilience: Clustering and Time-Series Forecasting in Intelligent Load Shedding
The growing demand for reliable electricity in universities necessitates intelligent energy management. This study proposes a machine learning-based load shedding framework for the University of Lagos, designed to optimize distribution and reduce waste. The methodology followed three main stages. First, a dataset of 3,648 hourly records from 55 buildings was compiled to develop building-level consumption models. Second, Principal Component Analysis was applied for dimensionality reduction, and …

I wrote up a talk I did recently: 'AI and Machine Learning in Libraries: Promising, But Not Ready Yet' https://www.openobjects.org.uk/2025/10/ai-and-machine-learning-in-libraries-promising-but-not-ready-yet

Predicting Chest Radiograph Findings from Electrocardiograms Using Interpretable Machine Learning
Julia Matejas, Olaf \.Zurawski, Nils Strodthoff, Juan Miguel Lopez Alcaraz
https://arxiv.org/abs/2509.17674

Predicting Chest Radiograph Findings from Electrocardiograms Using Interpretable Machine Learning
Purpose: Chest X-rays are essential for diagnosing pulmonary conditions, but limited access in resource-constrained settings can delay timely diagnosis. Electrocardiograms (ECGs), in contrast, are widely available, non-invasive, and often acquired earlier in clinical workflows. This study aims to assess whether ECG features and patient demographics can predict chest radiograph findings using an interpretable machine learning approach. Methods: Using the MIMIC-IV database, Extreme Gradient Boo…

Chargers vs. Vikings NFL player props: Self-learning AI backs Carson Wentz over 222.5 passing yards on TNF

https://www.cbssports.com/nfl/news/chargers-vikin…

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Charlotte Jennings (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Andrew Broad (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Lucy Godson (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Emily…

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Multimodal machine learning integrating histopathology and molecular data shows promise for cancer prognostication. We systematically reviewed studies combining whole slide images (WSIs) and high-throughput omics to predict overall survival. Searches of EMBASE, PubMed, and Cochrane CENTRAL (12/08/2024), plus citation screening, identified eligible studies. Data extraction used CHARMS; bias was assessed with PROBAST+AI; synthesis followed SWiM and PRISMA 2020. Protocol: PROSPERO (CRD42024594745)…

A Methodological Study on Data Representation for Machine Learning Modelling of Thermal Conductivity of Rare-Earth Oxides
Amiya Chowdhury, Acacio Rinc\'on Romero, Eduardo Aguilar-Bejarano, Halar Memon, Grazziela Figueredo, Tanvir Hussain
https://arxiv.org/abs/2509.18951

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials
Riccardo Farris, Emanuele Telari, Nongnuch Artrith, Konstantin Neyman, Albert Bruix
https://arxiv.org/abs/2509.19180

Advancing resistivity-chargeability modeling for complex subsurface characterization using machine learning and deep learning
Adedibu Sunny Akingboye, Andy Anderson Bery, Hui Tang, Ayokunle Olalekan Ige, Obinna Chigoziem Akakuru, Gabriel Abraham Bala, Mbuotidem David Dick
https://arxiv.org/abs/2509.17089

Advancing resistivity-chargeability modeling for complex subsurface characterization using machine learning and deep learning
Subsurface lithological heterogeneity presents challenges for traditional geophysical methods, particularly in resolving nonlinear electrical resistivity and induced polarization (IP) relationships. This study introduces a data-driven machine learning and deep learning (ML/DL) framework for predicting 2D IP chargeability models from resistivity, depth, and station distance, reducing reliance on field IP surveys. The framework integrates ensemble regressors with a one-dimensional convolutional n…

Climate-Adaptive and Cascade-Constrained Machine Learning Prediction for Sea Surface Height under Greenhouse Warming
Tianmu Zheng, Ru Chen, Xin Su, Gang Huang, Bingzheng Yan
https://arxiv.org/abs/2509.18741

Random functions as data compressors for machine learning of molecular processes
Jayashrita Debnath, Gerhard Hummer
https://arxiv.org/abs/2509.17937 https://

Random functions as data compressors for machine learning of molecular processes
Machine learning (ML) is rapidly transforming the way molecular dynamics simulations are performed and analyzed, from materials modeling to studies of protein folding and function. ML algorithms are often employed to learn low-dimensional representations of conformational landscapes and to cluster trajectories into relevant metastable states. Most of these algorithms require selecting a small number of features that describe the problem of interest. Although deep neural networks can tackle larg…

Distributed Functional Optimization and Learning on Banach Spaces: Generic Frameworks
Zhan Yu, Zhongjie Shi, Deming Yuan, Daniel W. C. Ho
https://arxiv.org/abs/2509.17554 https:…

Distributed Functional Optimization and Learning on Banach Spaces: Generic Frameworks
In this paper, we establish a distributed functional optimization (DFO) theory based on time-varying multi-agent networks. The vast majority of existing distributed optimization theories are developed based on Euclidean decision variables. However, for many scenarios in machine learning and statistical learning, such as reproducing kernel spaces or probability measure spaces that use functions or probability measures as fundamental variables, the development of existing distributed optimization…

Compact representation of transonic airfoil buffet flows with observable-augmented machine learning
Kai Fukami, Yuta Iwatani, Soju Maejima, Hiroyuki Asada, Soshi Kawai
https://arxiv.org/abs/2509.17306 …

Compact representation of transonic airfoil buffet flows with observable-augmented machine learning
Transonic buffet presents time-dependent aerodynamic characteristics associated with shock, turbulent boundary layer, and their interactions. Despite strong nonlinearities and a large degree of freedom, there exists a dominant dynamic pattern of a buffet cycle, suggesting the low dimensionality of transonic buffet phenomena. This study seeks a low-dimensional representation of transonic airfoil buffet at a high Reynolds number with machine learning. Wall-modeled large-eddy simulations of flow o…

Evaluating the Energy Efficiency of NPU-Accelerated Machine Learning Inference on Embedded Microcontrollers
Anastasios Fanariotis, Theofanis Orphanoudakis, Vasilis Fotopoulos
https://arxiv.org/abs/2509.17533

Evaluating the Energy Efficiency of NPU-Accelerated Machine Learning Inference on Embedded Microcontrollers
The deployment of machine learning (ML) models on microcontrollers (MCUs) is constrained by strict energy, latency, and memory requirements, particularly in battery-operated and real-time edge devices. While software-level optimizations such as quantization and pruning reduce model size and computation, hardware acceleration has emerged as a decisive enabler for efficient embedded inference. This paper evaluates the impact of Neural Processing Units (NPUs) on MCU-based ML execution, using the A…

A Nascent Taxonomy of Machine Learning in Intelligent Robotic Process Automation
Lukas Laakmann, Seyyid A. Ciftci, Christian Janiesch
https://arxiv.org/abs/2509.15730 https://…

A Nascent Taxonomy of Machine Learning in Intelligent Robotic Process Automation
Robotic process automation (RPA) is a lightweight approach to automating business processes using software robots that emulate user actions at the graphical user interface level. While RPA has gained popularity for its cost-effective and timely automation of rule-based, well-structured tasks, its symbolic nature has inherent limitations when approaching more complex tasks currently performed by human agents. Machine learning concepts enabling intelligent RPA provide an opportunity to broaden th…

Predictive Machine Learning to Increase the Throughput of Container Yards
Austin Ford Cooper
https://arxiv.org/abs/2509.16207 https://arxiv.org/pdf/2509.16…

Predictive Machine Learning to Increase the Throughput of Container Yards
This study seeks to improve the throughput rates for shipping container terminals. In the United States, shipping ports link the domestic economy to global markets and are vital to sustain supply chain flow and economic stability. Maritime shipping accounts for nearly half of the U.S.'s annual international trade, two thirds of which are represented by container shipping. Previous studies highlighted the capability of automation in enhancing container processing; however, unlike in European and…

Predicting Credit Spreads and Ratings with Machine Learning: The Role of Non-Financial Data
Yanran Wu, Xinlei Zhang, Quanyi Xu, Qianxin Yang, Chao Zhang
https://arxiv.org/abs/2509.19042

"“Papers with Code” went offline, the knowledge doesn’t have to" @…: https://blog.tib.eu/2025/10/02/papers-

Constraining Axion-Like Particle mediated Dark Matter with Observational Constraints: A Statistical and Machine Learning Approach
Prashant Thakur, Aravind Taridalu, Ishfaq Ahmad Rather, Tanech Klangburam, Chakrit Pongkitivanichkul
https://arxiv.org/abs/2509.18863

Causal Fuzzing for Verifying Machine Unlearning
Anna Mazhar, Sainyam Galhotra
https://arxiv.org/abs/2509.16525 https://arxiv.org/pdf/2509.16525

Causal Fuzzing for Verifying Machine Unlearning
As machine learning models become increasingly embedded in decision-making systems, the ability to "unlearn" targeted data or features is crucial for enhancing model adaptability, fairness, and privacy in models which involves expensive training. To effectively guide machine unlearning, a thorough testing is essential. Existing methods for verification of machine unlearning provide limited insights, often failing in scenarios where the influence is indirect. In this work, we propose CAFÉ, a ne…

P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices
Wei Fan, JinYi Yoon, Xiaochang Li, Huajie Shao, Bo Ji
https://arxiv.org/abs/2507.17228

P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices
Split Learning (SL) is an emerging privacy-preserving machine learning technique that enables resource constrained edge devices to participate in model training by partitioning a model into client-side and server-side sub-models. While SL reduces computational overhead on edge devices, it encounters significant challenges in heterogeneous environments where devices vary in computing resources, communication capabilities, environmental conditions, and privacy requirements. Although recent studie…

Towards a Robust Machine-Learning Pipeline for 21-cm Cosmology Data Analysis I: A Roadmap for Development and Demonstration of Robustness Against PSF Modeling Errors
Madhurima Choudhury, Jonathan C. Pober
https://arxiv.org/abs/2509.16280

Towards a Robust Machine-Learning Pipeline for 21-cm Cosmology Data Analysis I: A Roadmap for Development and Demonstration of Robustness Against PSF Modeling Errors
The 21-cm signal from the Epoch of Reionization (EoR) is a powerful probe of the evolution of the Universe. However, accurate measurements of the EoR signal from radio interferometric observations are sensitive to efficient foreground removal, mitigating radio-frequency interference and accounting for instrumental systematics. This work represents the first in a series of papers, where we will be introducing a novel ML based pipeline, step-by-step, to directly infer reionization parameters from…

Evidence for an Instability-induced Binary Merger in the Double-peaked, Helium-rich Type IIn #Supernova 2023zkd: https://iopscience.iop.org/article/10.3847/1538-4357/adea38/meta -> UC Santa Cruz-based survey uses AI to spot explosive stellar death by black hole: https://news.ucsc.edu/2025/08/black-hole-supernova/ - machine learning enables real-time detection of fleeting anomalies before they fade away.

Analysing the prevalence of tidal features in HSC-SSP using self-supervised representation learning
A. Desmons, S. Brough, F. Lanusse, L. Canepa, A. Khalid
https://arxiv.org/abs/2509.18274

Machine learning meets Singular Optics II: Single-pixel Detection of Structured Light
Purnesh Singh Badavath, Vijay Kumar
https://arxiv.org/abs/2509.16946 https://

Machine learning meets Singular Optics II: Single-pixel Detection of Structured Light
Structured light beams, including Laguerre-Gaussian (LG), Hermite-Gaussian (HG), and perfect vortex (PV) spatial modes, have been at the forefront of modern optics due to their potential in communications, metrology, and sensing. Traditional recognition methods often demand complex alignments and high-resolution imaging. Speckle-learned recognition (SLR) has emerged as a powerful alternative, exploiting the spatio-temporal speckle fields generated by light-diffuser interactions. This paper buil…

Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning
Xiaoran Xua, In-Ho Rab, Ravi Sankarc
https://arxiv.org/abs/2507.16845 https://arxiv.o…

Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning
Lung diseases, including lung cancer and COPD, are significant health concerns globally. Traditional diagnostic methods can be costly, time-consuming, and invasive. This study investigates the use of semi supervised learning methods for lung sound signal detection using a model combination of MFCC+CNN. By introducing semi supervised learning modules such as Mix Match, Co-Refinement, and Co Refurbishing, we aim to enhance the detection performance while reducing dependence on manual annotations.…

Deep Generative Learning of Magnetic Frustration in Artificial Spin Ice from Magnetic Force Microscopy Images
Arnab Neogi, Suryakant Mishra, Prasad P Iyer, Tzu-Ming Lu, Ezra Bussmann, Sergei Tretiak, Andrew Crandall Jones, Jian-Xin Zhu
https://arxiv.org/abs/2507.17726

Deep Generative Learning of Magnetic Frustration in Artificial Spin Ice from Magnetic Force Microscopy Images
Increasingly large datasets of microscopic images with atomic resolution facilitate the development of machine learning methods to identify and analyze subtle physical phenomena embedded within the images. In this work, microscopic images of honeycomb lattice spin-ice samples serve as datasets from which we automate the calculation of net magnetic moments and directional orientations of spin-ice configurations. In the first stage of our workflow, machine learning models are trained to accuratel…

When Machine Learning Meets Vulnerability Discovery: Challenges and Lessons Learned
Sima Arasteh, Christophe Hauser
https://arxiv.org/abs/2508.15042 https://

When Machine Learning Meets Vulnerability Discovery: Challenges and Lessons Learned
In recent years, machine learning has demonstrated impressive results in various fields, including software vulnerability detection. Nonetheless, using machine learning to identify software vulnerabilities presents new challenges, especially regarding the scale of data involved, which was not a factor in traditional methods. Consequently, in spite of the rise of new machine-learning-based approaches in that space, important shortcomings persist regarding their evaluation. First, researchers oft…

Modern search demands scalable personalisation. At this year's Berlin Buzzwords, Piotr Kobziakowski discussed how Vespa's multi-stage ranking and tensor framework can be used for hybrid queries, multimodal retrieval, and real-time machine learning. Learn how to deploy low-latency, high-relevance search systems at petabyte scale.
Watch the full session: https://youtu.be/362U7alsfuI?si=W5SGEO8MIa8vywGO
Berlin Buzzwords returns on 7-9 June 2026! Get 36% off with our Trust Us Ticket: https://tickets.plainschwarz.com/bbuzz26/c/8Hvk0ZvJA/

Simultaneous improved predictions of nuclear mass and charge radius via multi-task Gaussian process machine learning approach
Weihu Ye, Niu Wan
https://arxiv.org/abs/2507.17357 …

Simultaneous improved predictions of nuclear mass and charge radius via multi-task Gaussian process machine learning approach
We introduce a multi-task Gaussian process (GP) machine learning model to simultaneously predict nuclear masses and charge radii across the nuclear chart. Utilizing 12 physical input features, our multi-task GP consistently outperforms single-task learning, achieving root-mean-square deviations of 0.09 MeV for masses and 0.004 fm for charge radii. The model explicitly incorporates correlations between these observables. By employing the SHapley Additive exPlanations (SHAP) method, we interpret …

A Unified Framework for Inference with General Missingness Patterns and Machine Learning Imputation
Xingran Chen, Tyler McCormick, Bhramar Mukherjee, Zhenke Wu
https://arxiv.org/abs/2508.15162

A Unified Framework for Inference with General Missingness Patterns and Machine Learning Imputation
Pre-trained machine learning (ML) predictions have been increasingly used to complement incomplete data to enable downstream scientific inquiries, but their naive integration risks biased inferences. Recently, multiple methods have been developed to provide valid inference with ML imputations regardless of prediction quality and to enhance efficiency relative to complete-case analyses. However, existing approaches are often limited to missing outcomes under a missing-completely-at-random (MCAR)…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Comparison of Deterministic and Probabilistic Machine Learning Algorithms for Precise Dimensional...
Dipayan Sanpui, Anirban Chandra, Henry Chan, Sukriti Manna, Subramanian KRS Sankaranarayanan

Lessons from a Big-Bang Integration: Challenges in Edge Computing and Machine Learning
Alessandro Aneggi, Andrea Janes
https://arxiv.org/abs/2507.17270 htt…

Lessons from a Big-Bang Integration: Challenges in Edge Computing and Machine Learning
This experience report analyses a one year project focused on building a distributed real-time analytics system using edge computing and machine learning. The project faced critical setbacks due to a big-bang integration approach, where all components developed by multiple geographically dispersed partners were merged at the final stage. The integration effort resulted in only six minutes of system functionality, far below the expected 40 minutes. Through root cause analysis, the study identifi…

Chiefs vs. Giants props, bets, SportsLine Machine Learning Model AI predictions: Johnson over 20.5 receiving

https://www.cbssports.com/nfl/news/chiefs-

Latent learning: episodic memory complements parametric learning by enabling flexible reuse of experiences
Andrew Kyle Lampinen, Martin Engelcke, Yuxuan Li, Arslan Chaudhry, James L. McClelland
https://arxiv.org/abs/2509.16189

Latent learning: episodic memory complements parametric learning by enabling flexible reuse of experiences
When do machine learning systems fail to generalize, and what mechanisms could improve their generalization? Here, we draw inspiration from cognitive science to argue that one weakness of machine learning systems is their failure to exhibit latent learning -- learning information that is not relevant to the task at hand, but that might be useful in a future task. We show how this perspective links failures ranging from the reversal curse in language modeling to new findings on agent-based navig…

The Distribution Shift Problem in Transportation Networks using Reinforcement Learning and AI
Federico Taschin, Abderrahmane Lazaraq, Ozan K. Tonguz, Inci Ozgunes
https://arxiv.org/abs/2509.15291

The Distribution Shift Problem in Transportation Networks using Reinforcement Learning and AI
The use of Machine Learning (ML) and Artificial Intelligence (AI) in smart transportation networks has increased significantly in the last few years. Among these ML and AI approaches, Reinforcement Learning (RL) has been shown to be a very promising approach by several authors. However, a problem with using Reinforcement Learning in Traffic Signal Control is the reliability of the trained RL agents due to the dynamically changing distribution of the input data with respect to the distribution o…

M4SER: Multimodal, Multirepresentation, Multitask, and Multistrategy Learning for Speech Emotion Recognition
Jiajun He, Xiaohan Shi, Cheng-Hung Hu, Jinyi Mi, Xingfeng Li, Tomoki Toda
https://arxiv.org/abs/2509.18706

Quantum Autoencoder: An efficient approach to quantum feature map generation
Shengxin Zhuang, Yusen Wu, Xavier F. Cadet, Du Q. Huynh, Wei Liu, Philippe Charton, Cedric Damour, Frederic Cadet, Jingbo B. Wang
https://arxiv.org/abs/2509.19157

Quantum Autoencoder: An efficient approach to quantum feature map generation
Quantum machine learning methods often rely on fixed, hand-crafted quantum encodings that may not capture optimal features for downstream tasks. In this work, we study the power of quantum autoencoders in learning data-driven quantum representations. We first theoretically demonstrate that the quantum autoencoder method is efficient in terms of sample complexity throughout the entire training process. Then we numerically train the quantum autoencoder on 3 million peptide sequences, and evaluate…

Machine Learning-Assisted Nano-imaging and Spectroscopy of Phase Coexistence in a Wide-Bandgap Semiconductor
Alyssa Bragg, Fengdeng Liu, Zhifei Yang, Nitzan Hirshberg, Madison Garber, Brayden Lukaskawcez, Liam Thompson, Shane MacDonald, Hayden Binger, Devon Uram, Ashley Bucsek, Bharat Jalan, Alexander McLeod
https://arxiv.org/abs/2507.1767…

Machine Learning-Assisted Nano-imaging and Spectroscopy of Phase Coexistence in a Wide-Bandgap Semiconductor
Wide bandgap semiconductors with high room temperature mobilities are promising materials for high-power electronics. Stannate films provide wide bandgaps and optical transparency, although electron-phonon scattering can limit mobilities. In SrSnO3, epitaxial strain engineering stabilizes a high-mobility tetragonal phase at room temperature, resulting in a threefold increase in electron mobility among doped films. However, strain relaxation in thicker films leads to nanotextured coexistence of …

Hierarchical Reinforcement Learning Framework for Adaptive Walking Control Using General Value Functions of Lower-Limb Sensor Signals
Sonny T. Jones, Grange M. Simpson, Patrick M. Pilarski, Ashley N. Dalrymple
https://arxiv.org/abs/2507.16983

Hierarchical Reinforcement Learning Framework for Adaptive Walking Control Using General Value Functions of Lower-Limb Sensor Signals
Rehabilitation technology is a natural setting to study the shared learning and decision-making of human and machine agents. In this work, we explore the use of Hierarchical Reinforcement Learning (HRL) to develop adaptive control strategies for lower-limb exoskeletons, aiming to enhance mobility and autonomy for individuals with motor impairments. Inspired by prominent models of biological sensorimotor processing, our investigated HRL approach breaks down the complex task of exoskeleton contro…

A Curated Dataset and Deep Learning Approach for Minor Dent Detection in Vehicles
Danish Zia Baig, Mohsin Kamal
https://arxiv.org/abs/2508.15431 https://ar…

A Curated Dataset and Deep Learning Approach for Minor Dent Detection in Vehicles
Conventional car damage inspection techniques are labor-intensive, manual, and frequently overlook tiny surface imperfections like microscopic dents. Machine learning provides an innovative solution to the increasing demand for quicker and more precise inspection methods. The paper uses the YOLOv8 object recognition framework to provide a deep learning-based solution for automatically detecting microscopic surface flaws, notably tiny dents, on car exteriors. Traditional automotive damage inspec…

The Good, the Bad, and the Ugly of Atomistic Learning for "Clusters-to-Bulk" Generalization
Miko{\l}aj J. Gawkowski, Mingjia Li, Benjamin X. Shi, Venkat Kapil
https://arxiv.org/abs/2509.16601

The Good, the Bad, and the Ugly of Atomistic Learning for "Clusters-to-Bulk" Generalization
Training machine learning interatomic potentials (MLIPs) on total energies of molecular clusters using differential or transfer learning is becoming a popular route to extend the accuracy of correlated wave-function theory to condensed phases. A key challenge, however, lies in validation, as reference observables in finite-temperature ensembles are not available at the reference level. Here, we construct synthetic reference data from pretrained MLIPs and evaluate the generalizability of cluster…

System-Level Uncertainty Quantification with Multiple Machine Learning Models: A Theoretical Framework
Xiaoping Du
https://arxiv.org/abs/2509.16663 https://

System-Level Uncertainty Quantification with Multiple Machine Learning Models: A Theoretical Framework
ML models have errors when used for predictions. The errors are unknown but can be quantified by model uncertainty. When multiple ML models are trained using the same training points, their model uncertainties may be statistically dependent. In reality, model inputs are also random with input uncertainty. The effects of these types of uncertainty must be considered in decision-making and design. This study develops a theoretical framework that generates the joint distribution of multiple ML pre…

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
Md Min-Ha-Zul Abedin, Tazqia Mehrub
https://arxiv.org/abs/2507.16952

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
This study investigates the effectiveness of several machine learning algorithms for static malware detection using the EMBER dataset, which contains feature representations of Portable Executable (PE) files. We evaluate eight classification models: LightGBM, XGBoost, CatBoost, Random Forest, Extra Trees, HistGradientBoosting, k-Nearest Neighbors (KNN), and TabNet, under three preprocessing settings: original feature space, Principal Component Analysis (PCA), and Linear Discriminant Analysis (L…

Improving Active Learning for Melody Estimation by Disentangling Uncertainties
Aayush Jaiswal, Parampreet Singh, Vipul Arora
https://arxiv.org/abs/2509.17375 https://

Improving Active Learning for Melody Estimation by Disentangling Uncertainties
Estimating the fundamental frequency, or melody, is a core task in Music Information Retrieval (MIR). Various studies have explored signal processing, machine learning, and deep-learning-based approaches, with a very recent focus on utilizing uncertainty in active learning settings for melody estimation. However, these approaches do not investigate the relative effectiveness of different uncertainties. In this work, we follow a framework that disentangles aleatoric and epistemic uncertainties t…

Machine Learning in Near-Field Communication for 6G: A Survey
Amjad Iqbal, Ala'a Al-Habashna, Gabriel Wainer, Gary Boudreau
https://arxiv.org/abs/2509.16723 https://

Machine Learning in Near-Field Communication for 6G: A Survey
6G wireless communication networks are expected to use extremely large-scale antenna arrays (ELAAs) to support higher throughput, massive connectivity, and improved system performance. ELAAs would fundamentally alter wave characteristics, transforming them from plane waves into spherical waves, thereby operating in the near field. Near-field communications (NFC) offer unique advantages to enhance system performance, but also present significant challenges in channel modeling, computational comp…

Clapping: Removing Per-sample Storage for Pipeline Parallel Distributed Optimization with Communication Compression
Boao Kong, Xu Huang, Yuqi Xu, Yixuan Liang, Bin Wang, Kun Yuan
https://arxiv.org/abs/2509.19029

Unsupervised and probabilistic learning with Contrastive Local Learning Networks: The Restricted Kirchhoff Machine
Marcelo Guzman, Simone Ciarella, Andrea J. Liu
https://arxiv.org/abs/2509.15842

Unsupervised and probabilistic learning with Contrastive Local Learning Networks: The Restricted Kirchhoff Machine
Autonomous physical learning systems modify their internal parameters and solve computational tasks without relying on external computation. Compared to traditional computers, they enjoy distributed and energy-efficient learning due to their physical dynamics. In this paper, we introduce a self-learning resistor network, the Restricted Kirchhoff Machine, capable of solving unsupervised learning tasks akin to the Restricted Boltzmann Machine algorithm. The circuit relies on existing technology b…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/7]:
- Random functions as data compressors for machine learning of molecular processes
Jayashrita Debnath, Gerhard Hummer

SPADE: A Large Language Model Framework for Soil Moisture Pattern Recognition and Anomaly Detection in Precision Agriculture
Yeonju Lee, Rui Qi Chen, Joseph Oboamah, Po Nien Su, Wei-zhen Liang, Yeyin Shi, Lu Gan, Yongsheng Chen, Xin Qiao, Jing Li
https://arxiv.org/abs/2509.18123

Debiased maximum-likelihood estimators for hazard ratios under machine-learning adjustment
Takashi Hayakawa, Satoshi Asai
https://arxiv.org/abs/2507.17686 …

Debiased maximum-likelihood estimators for hazard ratios under machine-learning adjustment
Previous studies have shown that hazard ratios between treatment groups estimated with the Cox model are uninterpretable because the indefinite baseline hazard of the model fails to identify temporal change in the risk set composition due to treatment assignment and unobserved factors among multiple, contradictory scenarios. To alleviate this problem, especially in studies based on observational data with uncontrolled dynamic treatment and real-time measurement of many covariates, we propose ab…

Quantum Enhanced Anomaly Detection for ADS-B Data using Hybrid Deep Learning
Rani Naaman, Felipe Gohring de Magalhaes, Jean-Yves Ouattara, Gabriela Nicolescu
https://arxiv.org/abs/2509.15991

Quantum Enhanced Anomaly Detection for ADS-B Data using Hybrid Deep Learning
The emerging field of Quantum Machine Learning (QML) has shown promising advantages in accelerating processing speed and effectively handling the high dimensionality associated with complex datasets. Quantum Computing (QC) enables more efficient data manipulation through the quantum properties of superposition and entanglement. In this paper, we present a novel approach combining quantum and classical machine learning techniques to explore the impact of quantum properties for anomaly detection …

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- The Impact of Feature Scaling In Machine Learning: Effects on Regression and Classification Tasks
Pinheiro, de Oliveira, Silva, Saraiva, de Souza, Godoy, Ambrosio, Becker

PrediPrune: Reducing Verification Overhead in Souper with Machine Learning Driven Pruning
Ange-Thierry Ishimwe, Raghuveer Shivakumar, Heewoo Kim, Tamara Lehman, Joseph Izraelevitz
https://arxiv.org/abs/2509.16497

PrediPrune: Reducing Verification Overhead in Souper with Machine Learning Driven Pruning
Souper is a powerful enumerative superoptimizer that enhances the runtime performance of programs by optimizing LLVM intermediate representation (IR) code. However, its verification process, which relies on a computationally expensive SMT solver to validate optimization candidates, must explore a large search space. This large search space makes the verification process particularly expensive, increasing the burden to incorporate Souper into compilation tools. We propose PrediPrune, a stochasti…

BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning
Bingguang Lu, Hongsheng Hu, Yuantian Miao, Shaleeza Sohail, Chaoxiang He, Shuo Wang, Xiao Chen
https://arxiv.org/abs/2508.15541

BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning
Federated learning (FL) has been widely adopted as a decentralized training paradigm that enables multiple clients to collaboratively learn a shared model without exposing their local data. As concerns over data privacy and regulatory compliance grow, machine unlearning, which aims to remove the influence of specific data from trained models, has become increasingly important in the federated setting to meet legal, ethical, or user-driven demands. However, integrating unlearning into FL introdu…

LeakageDetector 2.0: Analyzing Data Leakage in Jupyter-Driven Machine Learning Pipelines
Owen Truong, Terrence Zhang, Arnav Marchareddy, Ryan Lee, Jeffery Busold, Michael Socas, Eman Abdullah AlOmar
https://arxiv.org/abs/2509.15971

LeakageDetector 2.0: Analyzing Data Leakage in Jupyter-Driven Machine Learning Pipelines
In software development environments, code quality is crucial. This study aims to assist Machine Learning (ML) engineers in enhancing their code by identifying and correcting Data Leakage issues within their models. Data Leakage occurs when information from the test dataset is inadvertently included in the training data when preparing a data science model, resulting in misleading performance evaluations. ML developers must carefully separate their data into training, evaluation, and test sets t…

Robust and Efficient Quantum Reservoir Computing with Discrete Time Crystal
Da Zhang, Xin Li, Yibin Guo, Haifeng Yu, Yirong Jin, Zhang-Qi Yin
https://arxiv.org/abs/2508.15230 ht…

Robust and Efficient Quantum Reservoir Computing with Discrete Time Crystal
The rapid development of machine learning and quantum computing has placed quantum machine learning at the forefront of research. However, existing quantum machine learning algorithms based on quantum variational algorithms face challenges in trainability and noise robustness. In order to address these challenges, we introduce a gradient-free, noise-robust quantum reservoir computing algorithm that harnesses discrete time crystal dynamics as a reservoir. We first calibrate the memory, nonlinear…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Validation-Free Sparse Learning: A Phase Transition Approach to Feature Selection
Sylvain Sardy, Maxime van Cutsem, Xiaoyu Ma

Eco-Friendly AI: Unleashing Data Power for Green Federated Learning
Mattia Sabella, Monica Vitali
https://arxiv.org/abs/2507.17241 https://

Eco-Friendly AI: Unleashing Data Power for Green Federated Learning
The widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) comes with a significant environmental impact, particularly in terms of energy consumption and carbon emissions. This pressing issue highlights the need for innovative solutions to mitigate AI's ecological footprint. One of the key factors influencing the energy consumption of ML model training is the size of the training dataset. ML models are often trained on vast amounts of data continuously generated by sensor…

SC2Tools: StarCraft II Toolset and Dataset API
Andrzej Bia{\l}ecki, Piotr Bia{\l}ecki, Piotr Sowi\'nski, Mateusz Budziak, Jan Gajewski
https://arxiv.org/abs/2509.18454 https…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[8/10]:
- CoLa: Learning to Interactively Collaborate with Large Language Models
Abhishek Sharma, Dan Goldwasser

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/7]:
- Deep Reinforcement Learning in Factor Investment
Junlin Liu
https://

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Minimax Adaptive Online Nonparametric Regression over Besov Spaces
Paul Liautaud (LPSM), Pierre Gaillard (Thoth, UGA), Olivier Wintenberger (LPSM)

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/10]:
- Joint Optimization of Multi-Objective Reinforcement Learning with Policy Gradient Based Algorithm
Qinbo Bai, Mridul Agarwal, Vaneet Aggarwal

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/10]:
- Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning witho...
Shaocong Ma, Ziyi Chen, Yi Zhou, Heng Huang

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- A Neural Difference-of-Entropies Estimator for Mutual Information
Haoran Ni, Martin Lotz

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- KM-GPT: An Automated Pipeline for Reconstructing Individual Patient Data from Kaplan-Meier Plots
Yao Zhao, Haoyue Sun, Yantian Ding, Yanxun Xu

Tutorial on the Probabilistic Unification of Estimation Theory, Machine Learning, and Generative AI
Mohammed Elmusrati
https://arxiv.org/abs/2508.15719 https://

Tutorial on the Probabilistic Unification of Estimation Theory, Machine Learning, and Generative AI
Extracting meaning from uncertain, noisy data is a fundamental problem across time series analysis, pattern recognition, and language modeling. This survey presents a unified mathematical framework that connects classical estimation theory, statistical inference, and modern machine learning, including deep learning and large language models. By analyzing how techniques such as maximum likelihood estimation, Bayesian inference, and attention mechanisms address uncertainty, the paper illustrates …

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Fast post-process Bayesian inference with Variational Sparse Bayesian Quadrature
Chengkun Li, Gr\'egoire Clart\'e, Martin J{\o}rgensen, Luigi Acerbi

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[10/10]:
- An Effective Strategy for Modeling Score Ordinality and Non-uniform Intervals in Automated Speaki...
Tien-Hong Lo, Szu-Yu Chen, Yao-Ting Sung, Berlin Chen

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[9/10]:
- HARPT: A Corpus for Analyzing Consumers' Trust and Privacy Concerns in Electronic Health Apps
Timoteo Kelly, Abdulkadir Korkmaz, Samuel Mallet, Connor Souders, Sadra Aliakbarpour, Praveen Rao

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/10]:
- Efficient Transition State Searches by Freezing String Method with Graph Neural Network Potentials
Jonah Marks, Joseph Gomes

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/10]:
- SINF: Semantic Neural Network Inference with Semantic Subgraphs
A. Q. M. Sazzad Sayyed, Francesco Restuccia

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/10]:
- A geometric framework for momentum-based optimizers for low-rank training
Steffen Schotth\"ofer, Timon Klein, Jonas Kusch

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/10]:
- Significativity Indices for Agreement Values
Alberto Casagrande, Francesco Fabris, Rossano Girometti, Roberto Pagliarini

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/10]:
- COMPOL: A Unified Neural Operator Framework for Scalable Multi-Physics Simulations
Yifei Sun, Tao Wang, Junqi Qu, Yushun Dong, Hewei Tang, Shibo Li

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/7]:
- Bilateral Distribution Compression: Reducing Both Data Size and Dimensionality
Dominic Broadbent, Nick Whiteley, Robert Allison, Tom Lovett

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/7]:
- Virtual Consistency for Audio Editing
Matthieu Cervera, Francesco Paissan, Mirco Ravanelli, Cem Subakan
…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/7]:
- Equip Pre-ranking with Target Attention by Residual Quantization
Yutong Li, Yu Zhu, Yichen Qiao, Ziyu Guan, Lv Shao, Tong Liu, Bo Zheng

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/7]:
- Decoding Uncertainty: The Impact of Decoding Strategies for Uncertainty Estimation in Large Langu...
Wataru Hashimoto, Hidetaka Kamigaito, Taro Watanabe

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/7]:
- Overfitting in Adaptive Robust Optimization
Karl Zhu, Dimitris Bertsimas
https:/…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- Beyond Single-Channel: Multichannel Signal Imaging for PPG-to-ECG Reconstruction with Vision Tran...
Xiaoyan Li, Shixin Xu, Faisal Habib, Arvind Gupta, Huaxiong Huang

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Flexible Coded Distributed Convolution Computing for Enhanced Straggler Resilience and Numerical ...
Shuo Tan, Rui Liu, Xuesong Han, XianLei Long, Kai Wan, Linqi Song, Yong Li

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks
Yuelin Wang, Kai Yi, Xinliang Liu, Yu Guang Wang, Shi Jin

GaussianPSL: A novel framework based on Gaussian Splatting for exploring the Pareto frontier in multi-criteria optimization
Phuong Mai Dinh, Van-Nam Huynh
https://arxiv.org/abs/2509.17889

GaussianPSL: A novel framework based on Gaussian Splatting for exploring the Pareto frontier in multi-criteria optimization
Multi-objective optimization (MOO) is essential for solving complex real-world problems involving multiple conflicting objectives. However, many practical applications - including engineering design, autonomous systems, and machine learning - often yield non-convex, degenerate, or discontinuous Pareto frontiers, which involve traditional scalarization and Pareto Set Learning (PSL) methods that struggle to approximate accurately. Existing PSL approaches perform well on convex fronts but tend to …

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Hybrid Deep Learning-Federated Learning Powered Intrusion Detection System for IoT/5G Advanced Ed...
Rasil Baidar, Sasa Maric, Robert Abbas

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Diffusion MRI with Machine Learning
Davood Karimi, Simon K. Warfield
https://

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/6]:
- PickleBall: Secure Deserialization of Pickle-based Machine Learning Models (Extended Report)
Kellas, Christou, Jiang, Li, Simon, David, Kemerlis, Davis, Yang

AI-Powered Machine Learning Approaches for Fault Diagnosis in Industrial Pumps
Khaled M. A. Alghtus, Ayad Gannan, Khalid M. Alhajri, Ali L. A. Al Jubouri, Hassan A. I. Al-Janahi
https://arxiv.org/abs/2508.15550

AI-Powered Machine Learning Approaches for Fault Diagnosis in Industrial Pumps
This study presents a practical approach for early fault detection in industrial pump systems using real-world sensor data from a large-scale vertical centrifugal pump operating in a demanding marine environment. Five key operational parameters were monitored: vibration, temperature, flow rate, pressure, and electrical current. A dual-threshold labeling method was applied, combining fixed engineering limits with adaptive thresholds calculated as the 95th percentile of historical sensor values. …

[2025-07-24 Thu (UTC), 67 new articles found for cs.LG Machine Learning]
toXiv_bot_toot

Probabilistic Graphical Models: A Concise Tutorial
Jacqueline Maasch, Willie Neiswanger, Stefano Ermon, Volodymyr Kuleshov
https://arxiv.org/abs/2507.17116

Probabilistic Graphical Models: A Concise Tutorial
Probabilistic graphical modeling is a branch of machine learning that uses probability distributions to describe the world, make predictions, and support decision-making under uncertainty. Underlying this modeling framework is an elegant body of theory that bridges two mathematical traditions: probability and graph theory. This framework provides compact yet expressive representations of joint probability distributions, yielding powerful generative models for probabilistic reasoning. This tut…

DeCo-SGD: Joint Optimization of Delay Staleness and Gradient Compression Ratio for Distributed SGD
Rongwei Lu, Jingyan Jiang, Chunyang Li, Haotian Dong, Xingguang Wei, Delin Cai, Zhi Wang
https://arxiv.org/abs/2507.17346

DeCo-SGD: Joint Optimization of Delay Staleness and Gradient Compression Ratio for Distributed SGD
Distributed machine learning in high end-to-end latency and low, varying bandwidth network environments undergoes severe throughput degradation. Due to its low communication requirements, distributed SGD (D-SGD) remains the mainstream optimizer in such challenging networks, but it still suffers from significant throughput reduction. To mitigate these limitations, existing approaches typically employ gradient compression and delayed aggregation to alleviate low bandwidth and high latency, respec…

Medical priority fusion: achieving dual optimization of sensitivity and interpretability in nipt anomaly detection
Xiuqi Ge, Zhibo Yao, Yaosong Du
https://arxiv.org/abs/2509.17924

Medical priority fusion: achieving dual optimization of sensitivity and interpretability in nipt anomaly detection
Clinical machine learning faces a critical dilemma in high-stakes medical applications: algorithms achieving optimal diagnostic performance typically sacrifice the interpretability essential for physician decision-making, while interpretable methods compromise sensitivity in complex scenarios. This paradox becomes particularly acute in non-invasive prenatal testing (NIPT), where missed chromosomal abnormalities carry profound clinical consequences yet regulatory frameworks mandate explainable A…