Tootfinder

Microsoft has once again been named a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning (DSML) Platforms.
https://azure.microsoft.com/en-us/blog

Microsoft recognized for second consecutive year as a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms | Microsoft Azure Blog
Find out how Microsoft Azure Machine Learning can help you optimize business processes, improve customer experiences, and drive innovation.

General Manager of Azure AI at Microsoft Don Scott shares a second consecutive year win in this post on the AI and Machine Learning portion of the official Azure blog. Gartner Group has identified Microsoft as a industry leader in data science and machine learning platforms.
"Microsoft recognized for second consecutive year as a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms"

Many are complaining about CISA removing the RSS feed for KEV. Just a reminder: we expose a lot of the API via RSS and Atom in vulnerability-lookup. KEV is included.
🔗 https://www.vulnerability-lookup.org/user-manual/feed-syndication/

From Tea Leaves to System Maps: Context-awareness in Monitoring Operational Machine Learning Models
Joran Leest, Claudia Raibulet, Patricia Lago, Ilias Gerostathopoulos
https://arxiv.org/abs/2506.10770

From Tea Leaves to System Maps: Context-awareness in Monitoring Operational Machine Learning Models
Machine learning (ML) models in production do not fail due to statistical anomalies in their input data; they fail due to contextual misalignment -- when their environment deviates from training assumptions, leading to unreliable predictions. Effective ML monitoring requires rich contextual information to move beyond detecting statistical shifts toward meaningful alerts and systematic root-cause analysis. Yet, surprisingly, despite extensive research in ML monitoring and related disciplines (dr…

Satellite Event
AIFoundry invites you to the 2nd edition of the AI Plumbers Conference, a meetup for low-level AI builders who value real conversations over keynote slides and for anyone working in modern data infrastructure, machine learning and open-source software projects. Connect, swap ideas and collaborate, free from the pressure of constant talks.
📅 When: June 15, 2025 – 10 am-5 pm
📍 Where: GLS Event Campus Berlin, Kastanienallee 82 | 10435 Berlin
Register now:

Towards Zero-Stall Matrix Multiplication on Energy-Efficient RISC-V Clusters for Machine Learning Acceleration
Luca Colagrande, Lorenzo Leone, Maximilian Coco, Andrei Deaconeasa, Luca Benini
https://arxiv.org/abs/2506.10921

Towards Zero-Stall Matrix Multiplication on Energy-Efficient RISC-V Clusters for Machine Learning Acceleration
The growing computational demands of machine learning (ML) workloads have driven the design of ML accelerators aiming at an optimal tradeoff between efficiency and flexibility. A widely explored architecture for flexible ML accelerators is based on clusters of lightweight instruction processors sharing multi-banked L1 memory, augmented with specialized instruction extensions for key ML-related computations, such as matrix multiplication (matmul). However, instruction extensions should be couple…

Black hole/quantum machine learning correspondence
Jae-Weon Lee, Zae Young Kim
https://arxiv.org/abs/2506.09678 https://arxiv.org/pdf…

Black hole/quantum machine learning correspondence
We explore a potential connection between the black hole information paradox and the double descent phenomenon in quantum machine learning. Information retrieval from Hawking radiation can be viewed through the lens of quantum linear regression over black hole microstates, with the Page time corresponding to the interpolation threshold, beyond which test error decreases despite overparameterization. Using the Marchenko-Pastur law, we derive the variance in test error for the quantum linear regr…

Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
Shriya Gumber, Lorena Alzate-Vargas, Benjamin T. Nebgen, Arjen van Veelen, Smit Kadvani, Tammie Gibson, Richard Messerly
https://arxiv.org/abs/2506.10211

Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
Machine learning interatomic potentials (MLIPs) are inherently limited by the accuracy of the training data, usually consisting of energies and forces obtained from quantum mechanical calculations, such as density functional theory (DFT). Since DFT itself is based on several approximations, MLIPs may inherit systematic errors that lead to discrepancies with experimental data. In this paper, we use a trajectory re-weighting technique to refine DFT pre-trained MLIPs to match the target experiment…

Learning Safe Control via On-the-Fly Bandit Exploration
Alexandre Capone, Ryan Cosner, Aaaron Ames, Sandra Hirche
https://arxiv.org/abs/2506.10279 https://…

Learning Safe Control via On-the-Fly Bandit Exploration
Control tasks with safety requirements under high levels of model uncertainty are increasingly common. Machine learning techniques are frequently used to address such tasks, typically by leveraging model error bounds to specify robust constraint-based safety filters. However, if the learned model uncertainty is very high, the corresponding filters are potentially invalid, meaning no control input satisfies the constraints imposed by the safety filter. While most works address this issue by assu…

Evaluation of Machine Learning Models in Student Academic Performance Prediction
A. G. R. Sandeepa, Sanka Mohottala
https://arxiv.org/abs/2506.08047 https:…

Evaluation of Machine Learning Models in Student Academic Performance Prediction
This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train…

An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia
Wishnu Badrawani
https://arxiv.org/abs/2506.10369

An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia
This paper evaluates the performance of prominent machine learning (ML) algorithms in predicting Indonesia's inflation using the payment system, capital market, and macroeconomic data. We compare the forecasting performance of each ML model, namely shrinkage regression, ensemble learning, and super vector regression, to that of the univariate time series ARIMA and SARIMA models. We examine various out-of-bag sample periods in each ML model to determine the appropriate data-splitting ratios for …

ChronoFlow - a Data-driven Model for #Gyrochronology: https://iopscience.iop.org/article/10.3847/1538-4357/adcd73 -> U of T Astronomers Pioneer Innovative Machine Learning Model to Determine the Ages of Stars: https://www.dunlap.utoronto.ca/u-of-t-astronomers-pioneer-innovative-machine-learning-model-to-determine-the-ages-of-stars/

SoK: Machine Unlearning for Large Language Models
Jie Ren, Yue Xing, Yingqian Cui, Charu C. Aggarwal, Hui Liu
https://arxiv.org/abs/2506.09227 https://

SoK: Machine Unlearning for Large Language Models
Large language model (LLM) unlearning has become a critical topic in machine learning, aiming to eliminate the influence of specific training data or knowledge without retraining the model from scratch. A variety of techniques have been proposed, including Gradient Ascent, model editing, and re-steering hidden representations. While existing surveys often organize these methods by their technical characteristics, such classifications tend to overlook a more fundamental dimension: the underlying…

A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications
Donglin Wang, Anjie Qiu, Qiuheng Zhou, Hans D. Schotten
https://arxiv.org/abs/2506.09512

A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications
The rapid advancement of Vehicle-to-Everything (V2X) communication is transforming Intelligent Transportation Systems (ITS), with 6G networks expected to provide ultra-reliable, low-latency, and high-capacity connectivity for Connected and Autonomous Vehicles (CAVs). Artificial Intelligence (AI) and Machine Learning (ML) have emerged as key enablers in optimizing V2X communication by enhancing network management, predictive analytics, security, and cooperative driving due to their outstanding p…

Scientific machine learning in Hydrology: a unified perspective
Adoubi Vincent De Paul Adombi
https://arxiv.org/abs/2506.06308 https://

Scientific machine learning in Hydrology: a unified perspective
Scientific machine learning (SciML) provides a structured approach to integrating physical knowledge into data-driven modeling, offering significant potential for advancing hydrological research. In recent years, multiple methodological families have emerged, including physics-informed machine learning, physics-guided machine learning, hybrid physics-machine learning, and data-driven physics discovery. Within each of these families, a proliferation of heterogeneous approaches has developed inde…

Prediction of steady states in a marine ecosystem model by a machine learning technique
Sarker Miraz Mahfuz, Thomas Slawig
https://arxiv.org/abs/2506.10475

Prediction of steady states in a marine ecosystem model by a machine learning technique
We used precomputed steady states obtained by a spin-up for a global marine ecosystem model as training data to build a mapping from the small number of biogeochemical model parameters onto the three-dimensional converged steady annual cycle. The mapping was performed by a conditional variational autoencoder (CVAE) with mass correction. Applied for test data, we show that the prediction obtained by the CVAE already gives a reasonable good approximation of the steady states obtained by a regular…

Evasion Attacks Against Bayesian Predictive Models
Pablo G. Arce, Roi Naveiro, David R\'ios Insua
https://arxiv.org/abs/2506.09640 https://

Evasion Attacks Against Bayesian Predictive Models
There is an increasing interest in analyzing the behavior of machine learning systems against adversarial attacks. However, most of the research in adversarial machine learning has focused on studying weaknesses against evasion or poisoning attacks to predictive models in classical setups, with the susceptibility of Bayesian predictive models to attacks remaining underexplored. This paper introduces a general methodology for designing optimal evasion attacks against such models. We investigate …

I like @…’s analysis here in many respects, both its broad conclusion (when the hype dies down, the useful parts remain and we take them for granted as normal tools), and this gem here:
Machine learning is well-suited for ❝any problem where we don’t actually know the rules and where the cost of a wrong answer is significantly lower than the benefit of a right answer.❞
1/2 https://infosec.exchange/@david_chisnall/112716199046923540

Machine learning accelerated finite-field simulations for electrochemical interfaces
Chaoqiang Feng, Bin Jiang
https://arxiv.org/abs/2506.10548 https://

Machine learning accelerated finite-field simulations for electrochemical interfaces
Electrochemical interfaces are of fundamental importance in electrocatalysis, batteries, and metal corrosion. Finite-field methods are one of most reliable approaches for modeling electrochemical interfaces in complete cells under realistic constant-potential conditions. However, previous finite-field studies have been limited to either expensive ab initio molecular dynamics or less accurate classical descriptions of electrodes and electrolytes. To overcome these limitations, we present a machi…

I don't know what it says about modern life but when I glanced at a message notification about 'weights' I wasn't sure if it was machine learning chat or the big strong lasses group

Machine Learning Left-Right Breaking from Gravitational Waves
William Searle, Csaba Bal\'azs, Yang Xiao, Yang Zhang
https://arxiv.org/abs/2506.09319 ht…

Machine Learning Left-Right Breaking from Gravitational Waves
First-order phase transitions in the early universe can generate stochastic gravitational waves (GWs), offering a unique probe of high-scale particle physics. The Left-Right Symmetric Model (LRSM), which restores parity symmetry at high energies and naturally incorporates the seesaw mechanism, allows for such transitions -- particularly during the spontaneous breaking of $SU(2)_R \times SU(2)_L \times U(1)_{B-L} \to SU(2)_L \times U(1)_Y$. This initial step, though less studied, is both theoret…

Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets
Md. Yeasin Rahat, Rajan Das Gupta, Nur Raisa Rahman, Sudipto Roy Pritom, Samiur Rahman Shakir, Md Imrul Hasan Showmick, Md. Jakir Hossen
https://arxiv.org/abs/2506.09851

Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets
The prediction of foreign exchange rates, such as the US Dollar (USD) to Bangladeshi Taka (BDT), plays a pivotal role in global financial markets, influencing trade, investments, and economic stability. This study leverages historical USD/BDT exchange rate data from 2018 to 2023, sourced from Yahoo Finance, to develop advanced machine learning models for accurate forecasting. A Long Short-Term Memory (LSTM) neural network is employed, achieving an exceptional accuracy of 99.449%, a Root Mean Sq…

Machine Learning-based quadratic closures for non-intrusive Reduced Order Models
Gabriele Codega, Anna Ivagnes, Nicola Demo, Gianluigi Rozza
https://arxiv.org/abs/2506.09830

Machine Learning-based quadratic closures for non-intrusive Reduced Order Models
In the present work, we introduce a data-driven approach to enhance the accuracy of non-intrusive Reduced Order Models (ROMs). In particular, we focus on ROMs built using Proper Orthogonal Decomposition (POD) in an under-resolved and marginally-resolved regime, i.e. when the number of modes employed is not enough to capture the system dynamics. We propose a method to re-introduce the contribution of neglected modes through a quadratic correction term, given by the action of a quadratic operator…

Introduction to Predictive Coding Networks for Machine Learning
Mikko Stenlund
https://arxiv.org/abs/2506.06332 https://arxiv.org/pdf…

Introduction to Predictive Coding Networks for Machine Learning
Predictive coding networks (PCNs) constitute a biologically inspired framework for understanding hierarchical computation in the brain, and offer an alternative to traditional feedforward neural networks in ML. This note serves as a quick, onboarding introduction to PCNs for machine learning practitioners. We cover the foundational network architecture, inference and learning update rules, and algorithmic implementation. A concrete image-classification task (CIFAR-10) is provided as a benchmark…

Distinguishing Orbiting and Infalling Dark Matter Particles with Machine Learning
Ze'ev Vladimir, Calvin Osinga, Benedikt Diemer, Edgar M. Salazar, Eduardo Rozo
https://arxiv.org/abs/2506.09146

Distinguishing Orbiting and Infalling Dark Matter Particles with Machine Learning
Dark matter halos are typically defined as spheres that enclose some overdensity, but these sharp, somewhat arbitrary boundaries introduce non-physical artifacts such as backsplash halos, pseudo-evolution, and an incomplete accounting of halo mass. A more physically motivated alternative is to define halos as the collection of particles that are physically orbiting within their potential well. However, existing methods to classify particles as orbiting or infalling suffer from trade-offs betwee…

Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks
Rohit Sonker, Alexandre Capone, Andrew Rothstein, Hiro Josep Farre Kaga, Egemen Kolemen, Jeff Schneider
https://arxiv.org/abs/2506.10287

Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks
Machine learning algorithms often struggle to control complex real-world systems. In the case of nuclear fusion, these challenges are exacerbated, as the dynamics are notoriously complex, data is poor, hardware is subject to failures, and experiments often affect dynamics beyond the experiment's duration. Existing tools like reinforcement learning, supervised learning, and Bayesian optimization address some of these challenges but fail to provide a comprehensive solution. To overcome these limi…

Data-driven Identification of Attractors Using Machine Learning
Marcio Gameiro, Brittany Gelb, William Kalies, Miroslav Kramar, Konstantin Mischaikow, Paul Tatasciore
https://arxiv.org/abs/2506.06492

Data-driven Identification of Attractors Using Machine Learning
In this paper we explore challenges in developing a topological framework in which machine learning can be used to robustly characterize global dynamics. Specifically, we focus on learning a useful discretization of the phase space of a flow on compact, hyperrectangle in $\mathbb{R}^n$ from a neural network trained on labeled orbit data. A characterization of the structure of the global dynamics is obtained from approximations of attracting neighborhoods provided by the phase space discretizati…

TS-PIELM: Time-Stepping Physics-Informed Extreme Learning Machine Facilitates Soil Consolidation Analyses
He Yang, Fei Ren, Hai-Sui Yu, Xueyu Geng, Pei-Zhi Zhuang
https://arxiv.org/abs/2506.08381

TS-PIELM: Time-Stepping Physics-Informed Extreme Learning Machine Facilitates Soil Consolidation Analyses
Accuracy and efficiency of the conventional physics-informed neural network (PINN) need to be improved before it can be a competitive alternative for soil consolidation analyses. This paper aims to overcome these limitations by proposing a highly accurate and efficient physics-informed machine learning (PIML) approach, termed time-stepping physics-informed extreme learning machine (TS-PIELM). In the TS-PIELM framework the consolidation process is divided into numerous time intervals, which help…

Local surrogates for quantum machine learning
Sreeraj Rajindran Nair, Christopher Ferrie
https://arxiv.org/abs/2506.09425 https://arx…

Local surrogates for quantum machine learning
Surrogates have been proposed as classical simulations of the pretrained quantum learning models, which are capable of mimicking the input-output relation inherent in the quantum model. Quantum hardware within this framework is used for training and for generating the classical surrogates. Inference is relegated to the classical surrogate, hence alleviating the extra quantum computational cost once training is done. Taking inspiration from interpretable models, we introduce a local surrogation …

Artificial intelligence-enabled precision medicine for inflammatory skin diseases
Alice Tang, Maria Wei, Anna Haemel, Cindy La, Marina Sirota, Ernest Y. Lee
https://arxiv.org/abs/2505.09527

Artificial intelligence-enabled precision medicine for inflammatory skin diseases
Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning approaches offer opportunities to enhance the diagnosis and treatment of inflammatory skin diseases, including atopic dermatitis, psoriasis, hidradenitis suppurativa, and autoimmune connective tissue disease. This review examines the current landscape of AI applications for inflammatory skin diseases and explores how generative AI and machine learnin…

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
Zhaoxuan Kan, Husheng Han, Shangyi Shi, Tenghui Hua, Hang Lu, Xiaowei Li, Jianan Mu, Xing Hu
https://arxiv.org/abs/2506.10399

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
Graph Convolutional Neural Networks (GCNs) have gained widespread popularity in various fields like personal healthcare and financial systems, due to their remarkable performance. Despite the growing demand for cloud-based GCN services, privacy concerns over sensitive graph data remain significant. Homomorphic Encryption (HE) facilitates Privacy-Preserving Machine Learning (PPML) by allowing computations to be performed on encrypted data. However, HE introduces substantial computational overhea…

Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization
Ana Lawry Aguila, Peirong Liu, Oula Puonti, Juan Eugenio Iglesias
https://arxiv.org/abs/2506.10233

Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization
Supervised machine learning has enabled accurate pathology detection in brain MRI, but requires training data from diseased subjects that may not be readily available in some scenarios, for example, in the case of rare diseases. Reconstruction-based unsupervised anomaly detection, in particular using diffusion models, has gained popularity in the medical field as it allows for training on healthy images alone, eliminating the need for large disease-specific cohorts. These methods assume that a …

Machine Learning for the Cluster Reconstruction in the CALIFA Calorimeter at R3B
Tobias Jenegger, Nicole Hartman, Roman Gernhaeuser, Lukas Heinrich, Laura Fabbietti
https://arxiv.org/abs/2506.09088

Machine Learning for the Cluster Reconstruction in the CALIFA Calorimeter at R3B
The R3B experiment at FAIR studies nuclear reactions using high-energy radioactive beams. One key detector in R3B is the CALIFA calorimeter consisting of 2544 CsI(Tl) scintillator crystals designed to detect light charged particles and gamma rays with an energy resolution in the per cent range after Doppler correction. Precise cluster reconstruction from sparse hit patterns is a crucial requirement. Standard algorithms typically use fixed cluster sizes or geometric thresholds. To enhance perfor…

This https://arxiv.org/abs/2505.13182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
Ryoichi Asashiba, Reiji Kozuma, Hirokazu Iwasawa
https://arxiv.org/abs/2506.08338

midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
The use of appropriate methods of Interpretable Machine Learning (IML) and eXplainable Artificial Intelligence (XAI) is essential for adopting black-box predictive models in fields where model and prediction explainability is required. As a novel tool for interpreting black-box models, we introduce the R package midr, which implements Maximum Interpretation Decomposition (MID). MID is a functional decomposition approach that derives a low-order additive representation of a black-box model by mi…

Identifying Merger-Driven Long Gamma-Ray Bursts based on Machine Learning
Si-Yuan Zhu, Hui-Ying Deng, Fu-Wen Zhang, Qian-Zi Mo, Pak-Hin Thomas Tam
https://arxiv.org/abs/2506.08675

Identifying Merger-Driven Long Gamma-Ray Bursts based on Machine Learning
Gamma-ray bursts (GRBs) are classified as Type I GRBs originated from compact binary mergers and Type II GRBs originated from massive collapsars. While Type I GRBs are typically shorter than 2 seconds, recent observations suggest that some extend to tens of seconds, forming a potential subclass, Type IL GRBs. However, apart from their association with kilonovae, so far no rapid identification is possible. Given the uncertainties and limitations of optical and infrared afterglow observations, an…

Physics-informed Machine Learning Analysis for Nanoscale Grain Mapping by Synchrotron Laue Microdiffraction
Ka Hung Chan, Xinyue Huang, Nobumichi Tamura, Xian Chen
https://arxiv.org/abs/2506.10937

Physics-informed Machine Learning Analysis for Nanoscale Grain Mapping by Synchrotron Laue Microdiffraction
Understanding the grain morphology, orientation distribution, and crystal structure of nanocrystals is essential for optimizing the mechanical and physical properties of functional materials. Synchrotron X-ray Laue microdiffraction is a powerful technique for characterizing crystal structures and orientation mapping using focused X-rays. However, when grain sizes are smaller than the beam size, mixed peaks in the Laue pattern from neighboring grains limit the resolution of grain morphology mapp…

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC
CMS Collaboration
https://arxiv.org/abs/2506.08826

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC
A novel solution is presented for the problem of estimating the backgrounds of a signal search using observed data while simultaneously maximizing the sensitivity of the search to the signal. The ``ABCD method'' provides a reliable framework for background estimation by partitioning events into one signal-enhanced region (A) and three background-enhanced control regions (B, C, and D) via two statistically independent variables. In practice, even slight correlations between the two variables can…

This https://arxiv.org/abs/2410.08850 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Learning to Stop: Deep Learning for Mean Field Optimal Stopping
Optimal stopping is a fundamental problem in optimization with applications in risk management, finance, robotics, and machine learning. We extend the standard framework to a multi-agent setting, named multi-agent optimal stopping (MAOS), where agents cooperate to make optimal stopping decisions in a finite-space, discrete-time environment. Since solving MAOS becomes computationally prohibitive as the number of agents is very large, we study the mean-field optimal stopping (MFOS) problem, obtai…

Application of quantum machine learning using variational quantum classifier in accelerator physics
He-Xing Yin, Zhi-Yuan Hu, Huan-Huan Zeng, Jia-Bao Guan, Ji-ke Wang
https://arxiv.org/abs/2506.06662

Application of quantum machine learning using variational quantum classifier in accelerator physics
Quantum machine learning algorithms aim to take advantage of quantum computing to improve classical machine learning algorithms. In this paper, we have applied a quantum machine learning algorithm, the variational quantum classifier for the first time in accelerator physics. Specifically, we utilized the variational quantum classifier to evaluate the dynamic aperture of a diffraction-limited storage ring. It has been demonstrated that the variational quantum classifier can achieve good accuracy…

Machine learning-based correlation analysis of decadal cyclone intensity with sea surface temperature: data and tutorial
Jingyang Wu, Rohitash Chandra
https://arxiv.org/abs/2506.09254

Machine learning-based correlation analysis of decadal cyclone intensity with sea surface temperature: data and tutorial
The rising number of extreme climate events in the past decades has motivated the need for a thorough consideration of tropical cyclone genesis and intensity, given the sea-surface temperature (SST). In this paper, we present an analysis of the relationship between the increasing global SST with cyclone genesis using linear regression machine learning models. We extract and curate a dataset of tropical cyclones across selected ocean basins with their associated SST over the past 40 years. We pr…

AIFoundry invites you to the 2nd edition of the AI Plumbers Conference, a meetup for low-level AI builders who value real conversations over keynote slides and for anyone working in modern data infrastructure, machine learning and open-source software projects. Connect, swap ideas and collaborate, free from the pressure of constant talks.
📅 When: June 15, 2025 – 10 am
📍 Where: GLS Event Campus Berlin, Kastanienallee 82 | 10435 Berlin
Register now:

AI Plumbers conference: 2nd edition · Luma
AIFoundry is bringing you the AI Plumbers Conference: 2nd edition — an open source meetup for low-level AI builders to dive deep into "the plumbing" of modern…

Liquid and solid layers in a thermal deep learning machine
Gang Huang, Lai Shun Chan, Hajime Yoshino, Ge Zhang, Yuliang Jin
https://arxiv.org/abs/2506.06789

Liquid and solid layers in a thermal deep learning machine
Based on deep neural networks (DNNs), deep learning has been successfully applied to many problems, but its mechanism is still not well understood -- especially the reason why over-parametrized DNNs can generalize. A recent statistical mechanics theory on supervised learning by a prototypical multi-layer perceptron (MLP) on some artificial learning scenarios predicts that adjustable parameters of over-parametrized MLPs become strongly constrained by the training data close to the input/output b…

Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system
Sanna Jarl, Jens Sj\"olund, Robert J. W. Frost, Anders Holst, Jonathan J. S. Scragg
https://arxiv.org/abs/2506.05999

Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system
Self-driving labs (SDLs), employing automation and machine learning (ML) to accelerate experimental procedures, have enormous potential in the discovery of new materials. However, in thin film science, SDLs are mainly restricted to solution-based synthetic methods which are easier to automate but cannot access the broad chemical space of inorganic materials. This work presents an SDL based on magnetron co-sputtering. We are using combinatorial frameworks, obtaining accurate composition maps on …

The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity - Apple Machine Learning Research
https://machinelearning.apple.com/research/illusion-of-thinking

The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
Recent generations of frontier language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes…

Nach Rücksprache mit den Buch-Herausgebern und dem Verlag darf ich meine jüngste (und ausführlichste) Publikation zu generativen Machine-Learning-Systemen (GMLS) vom Januar 2025 bereits jetzt online zur Verfügung stellen: https://zenodo.org/records/15042499

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Jonas Svedas, Hannah Watson, Nathan Laubeuf, Diksha Moolchandani, Abubakr Nada, Arjun Singh, Dwaipayan Biswas, James Myers, Debjyoti Bhattacharjee
https://arxiv.org/abs/2506.09275

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Distributed deep neural networks (DNNs) have become a cornerstone for scaling machine learning to meet the demands of increasingly complex applications. However, the rapid growth in model complexity far outpaces CMOS technology scaling, making sustainable and efficient system design a critical challenge. Addressing this requires coordinated co-design across software, hardware, and technology layers. Due to the prohibitive cost and complexity of deploying full-scale training systems, simulators …

Mitigating Polarization Leakage in Gas Pixel Detectors through Hybrid Machine Learning and Analytic Event Reconstruction
Nicol\'o Cibrario, Michela Negro, Raffaella Bonino, Nikita Moriakov, Luca Baldini, Niccol\'o Di Lalla, Alessandro Di Marco, Sergio Fabiani, Andrea Frass\'a, Alessio Gorgi, Fabio La Monaca, Luca Latronico, Simone Maldera, Alberto Manfreda, Fabio Muleri, Nicola Omodei, John Rankin, Carmelo Sgr\'o, Stefano Silvestri, Paolo Soffitta, Stefano Tugliani

Mitigating Polarization Leakage in Gas Pixel Detectors through Hybrid Machine Learning and Analytic Event Reconstruction
Spatially resolved polarization measurements of extended X-ray sources are expanding our understanding of the emission mechanisms and magnetic field properties involved. Such measurements have been possible in the past few years thanks to the Imaging X-ray Polarimetry Explorer (IXPE). However, the analysis of extended sources suffers a systematic effect known as polarization leakage, which artificially affects the measured polarization signal. To address this issue, we built a hybrid reconstruc…

The TESS Ten Thousand Catalog: 10,001 uniformly-vetted and -validated Eclipsing Binary Stars detected in Full-Frame Image data by machine learning and analyzed by citizen scientists
Veselin B. Kostov, Brian P. Powell, Aline U. Fornear, Marco Z. Di Fraia, Robert Gagliano, Thomas L. Jacobs, Julien S. de Lambilly, Hugo A. Durantini Luca, Steven R. Majewski, Mark Omohundro, Jerome Orosz, Saul A. Rappaport, Ryan Salik, Donald Short, William Welsh, Svetoslav Alexandrov, Cledison Marcos da Si…

The TESS Ten Thousand Catalog: 10,001 uniformly-vetted and -validated Eclipsing Binary Stars detected in Full-Frame Image data by machine learning and analyzed by citizen scientists
The Transiting Exoplanet Survey Satellite (TESS) has surveyed nearly the entire sky in Full-Frame Image mode with a time resolution of 200 seconds to 30 minutes and a temporal baseline of at least 27 days. In addition to the primary goal of discovering new exoplanets, TESS is exceptionally capable at detecting variable stars, and in particular short-period eclipsing binaries which are relatively common, making up a few percent of all stars, and represent powerful astrophysical laboratories for …

[2025-06-13 Fri (UTC), 10 new articles found for stat.ML Machine Learning]
toXiv_bot_toot

A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)
https://arxiv.org/abs/2506.08153

A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems. This paper showcases the first step for creating the metrics-based architectural mode…

GEARS H: Accurate machine-learned Hamiltonians for next-generation device-scale modeling
Anubhab Haldar, Ali K. Hamze, Nikhil Sivadas, Yongwoo Shin
https://arxiv.org/abs/2506.10298

GEARS H: Accurate machine-learned Hamiltonians for next-generation device-scale modeling
We introduce GEARS H, a state-of-the-art machine-learning Hamiltonian framework for large-scale electronic structure simulations. Using GEARS H, we present a statistical analysis of the hole concentration induced in defective $\mathrm{WSe}_2$ interfaced with Ni-doped amorphous $\mathrm{HfO}_2$ as a function of the Ni doping rate, system density, and Se vacancy rate in 72 systems ranging from 3326 to 4160 atoms-a quantity and scale of interface electronic structure calculation beyond the reach o…

AI-Driven SEEG Channel Ranking for Epileptogenic Zone Localization
Saeed Hashemi, Genchang Peng, Mehrdad Nourani, Omar Nofal, Jay Harvey
https://arxiv.org/abs/2506.09255

AI-Driven SEEG Channel Ranking for Epileptogenic Zone Localization
Stereo-electroencephalography (SEEG) is an invasive technique to implant depth electrodes and collect data for pre-surgery evaluation. Visual inspection of signals recorded from hundreds of channels is time consuming and inefficient. We propose a machine learning approach to rank the impactful channels by incorporating clinician's selection and computational finding. A classification model using XGBoost is trained to learn the discriminative features of each channel during ictal periods. Then, …

Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction
Shuai Li, Azarakhsh Keipour, Sicong Zhao, Srinath Rajagopalan, Charles Swan, Kostas E. Bekris
https://arxiv.org/abs/2506.09765

Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction
Warehouse automation plays a pivotal role in enhancing operational efficiency, minimizing costs, and improving resilience to workforce variability. While prior research has demonstrated the potential of machine learning (ML) models to increase picking success rates in large-scale robotic fleets by prioritizing high-probability picks and packages, these efforts primarily focused on predicting success probabilities for picks sampled using heuristic methods. Limited attention has been given, howev…

Prompt-Guided Latent Diffusion with Predictive Class Conditioning for 3D Prostate MRI Generation
Emerson P. Grabke, Masoom A. Haider, Babak Taati
https://arxiv.org/abs/2506.10230 …

Prompt-Guided Latent Diffusion with Predictive Class Conditioning for 3D Prostate MRI Generation
Latent diffusion models (LDM) could alleviate data scarcity challenges affecting machine learning development for medical imaging. However, medical LDM training typically relies on performance- or scientific accessibility-limiting strategies including a reliance on short-prompt text encoders, the reuse of non-medical LDMs, or a requirement for fine-tuning with large data volumes. We propose a Class-Conditioned Efficient Large Language model Adapter (CCELLA) to address these limitations. CCELLA …

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation
Simon Lang, Martin Leutbecher, Pedro Maciel
https://arxiv.org/abs/2506.10868

A multi-scale loss formulation for learning a probabilistic model with proper score optimisation
We assess the impact of a multi-scale loss formulation for training probabilistic machine-learned weather forecasting models. The multi-scale loss is tested in AIFS-CRPS, a machine-learned weather forecasting model developed at the European Centre for Medium-Range Weather Forecasts (ECMWF). AIFS-CRPS is trained by directly optimising the almost fair continuous ranked probability score (afCRPS). The multi-scale loss better constrains small scale variability without negatively impacting forecast …

This https://arxiv.org/abs/2204.04198 has been replaced.
link: https://scholar.google.com/scholar?q=a

Modern applications of machine learning in quantum sciences
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, gen…

Deep Potential-Driven Molecular Dynamics of CO Ice Analogues: Investigating Desorption Following Vibrational Excitation
Maxime Infuso, Samuel Del Fr\'e, Gilberto A. Alou, Mathieu Bertin, Jean-Hugues Fillion, Alejandro Rivero Santamar\'ia, Maurice Monnerville
https://arxiv.org/abs/2506.10882…

Deep Potential-Driven Molecular Dynamics of CO Ice Analogues: Investigating Desorption Following Vibrational Excitation
We present a new deep learning-based machine learning potential (MLP) for molecular dynamics simulations of solid carbon monoxide (CO), capable of accurately describing CO vibrations both in the fundamental state and in highly excited vibrational states, up to approximately v = 40. The MLP is based on the combination of high-dimensional neural network atomic potentials using the DeePMD-kit package, trained on prior ab initio molecular dynamics (AIMD) data, with selective treatment of the excite…

Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection
Gabriele Digregorio, Elisabetta Cainazzo, Stefano Longari, Michele Carminati, Stefano Zanero
https://arxiv.org/abs/2506.04978

Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection
The challenges derived from the data-intensive nature of machine learning in conjunction with technologies that enable novel paradigms such as V2X and the potential offered by 5G communication, allow and justify the deployment of Federated Learning (FL) solutions in the vehicular intrusion detection domain. In this paper, we investigate the effects of integrating FL strategies into the machine learning-based intrusion detection process for on-board vehicular networks. Accordingly, we propose a …

Employing Discrete Fourier Transform in Representational Learning
Raoof HojatJalali, Edmondo Trentin
https://arxiv.org/abs/2506.06765 https://

Employing Discrete Fourier Transform in Representational Learning
Image Representation learning via input reconstruction is a common technique in machine learning for generating representations that can be effectively utilized by arbitrary downstream tasks. A well-established approach is using autoencoders to extract latent representations at the network's compression point. These representations are valuable because they retain essential information necessary for reconstructing the original input from the compressed latent space. In this paper, we propose an…

Enhanced randomized Douglas-Rachford method: Improved probabilities and adaptive momentum
Liqi Guo, Ruike Xiang, Deren Han, Jiaxin Xie
https://arxiv.org/abs/2506.10261

Enhanced randomized Douglas-Rachford method: Improved probabilities and adaptive momentum
Randomized iterative methods have gained recent interest in machine learning and signal processing for solving large-scale linear systems. One such example is the randomized Douglas-Rachford (RDR) method, which updates the iterate by reflecting it through two randomly selected hyperplanes and taking a convex combination with the current point. In this work, we enhance RDR by introducing improved sampling strategies and an adaptive heavy-ball momentum scheme. Specifically, we incorporate without…

This https://arxiv.org/abs/2505.13182 has been replaced.
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Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

Flexible and Efficient Drift Detection without Labels
Nelvin Tan, Yu-Ching Shih, Dong Yang, Amol Salunkhe
https://arxiv.org/abs/2506.08734 https://

Flexible and Efficient Drift Detection without Labels
Machine learning models are being increasingly used to automate decisions in almost every domain, and ensuring the performance of these models is crucial for ensuring high quality machine learning enabled services. Ensuring concept drift is detected early is thus of the highest importance. A lot of research on concept drift has focused on the supervised case that assumes the true labels of supervised tasks are available immediately after making predictions. Controlling for false positives while…

BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis
Surya Jasper, Minh Luu, Evan Pan, Aakash Tyagi, Michael Quinn, Jiang Hu, David Kebo Houngninou
https://arxiv.org/abs/2506.10501

BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis
Hardware complexity continues to strain verification resources, motivating the adoption of machine learning (ML) methods to improve debug efficiency. However, ML-assisted debugging critically depends on diverse and scalable bug datasets, which existing manual or automated bug insertion methods fail to reliably produce. We introduce BugGen, a first of its kind, fully autonomous, multi-agent pipeline leveraging Large Language Models (LLMs) to systematically generate, insert, and validate realisti…

Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence
John Violos, Konstantina-Christina Diamanti, Ioannis Kompatsiaris, Symeon Papadopoulos
https://arxiv.org/abs/2506.01869

Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence
Frugal Machine Learning (FML) refers to the practice of designing Machine Learning (ML) models that are efficient, cost-effective, and mindful of resource constraints. This field aims to achieve acceptable performance while minimizing the use of computational resources, time, energy, and data for both training and inference. FML strategies can be broadly categorized into input frugality, learning process frugality, and model frugality, each focusing on reducing resource consumption at different…

The impact of extracurricular education on socioeconomic mobility in Japan: an application of causal machine learning
Yang Qiang
https://arxiv.org/abs/2506.07421

The impact of extracurricular education on socioeconomic mobility in Japan: an application of causal machine learning
This paper explores the socioeconomic impacts of extracurricular education, specifically private tutoring, on social mobility in Japan. Using data from the 2015 National Survey on Social Stratification and Social Mobility (SSM), we employed a causal machine learning approach to evaluate this educational intervention on income, educational attainment, and occupational prestige. Our research suggests that while shadow education holds the potential for positive socioeconomic impacts, its benefits …

Situated Bayes -- Feminist and Pluriversal Perspectives on Bayesian Knowledge
Juni Schindler, Goda Klumbyt\.e, Matthew Fuller
https://arxiv.org/abs/2506.09472

Situated Bayes -- Feminist and Pluriversal Perspectives on Bayesian Knowledge
This is the introduction and lead article to the Situated Bayes special issue of Computational Culture. The article introduces Bayes' Theorem and aspects of its contemporary uses, for instance in machine learning. A mathematical discussion is developed alongside a consideration of Bayes Theorem in relation to critical theories of knowledge, specifically the discussion of situated knowledge in feminist theories of science, pluriversal knowledge in decolonial theory, and critical approaches to ma…

This https://arxiv.org/abs/2505.04947 has been replaced.
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DFPL: Decentralized Federated Prototype Learning Across Heterogeneous Data Distributions
Federated learning is a distributed machine learning paradigm through centralized model aggregation. However, standard federated learning relies on a centralized server, making it vulnerable to server failures. While existing solutions utilize blockchain technology to implement Decentralized Federated Learning (DFL), the statistical heterogeneity of data distributions among clients severely degrades the performance of DFL. Driven by this issue, this paper proposes a decentralized federated prot…

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture
Logan M. Brabson, Andrew J. Medford, David S. Sholl
https://arxiv.org/abs/2506.09256

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture
Direct air capture (DAC) with solid sorbents such as metal-organic frameworks (MOFs) is a promising approach for negative carbon emissions. Computational materials screening can help identify promising materials from the vast chemical space of potential sorbents. Experiments have shown that MOF framework flexibility and deformation induced by adsorbate molecules can drastically affect adsorption properties such as capacity and selectivity. Force field (FF) models are commonly used as surrogates…

Observational Insights on DBI K-essence Models Using Machine Learning and Bayesian Analysis
Samit Ganguly, Arijit Panda, Eduardo Guendelman, Debashis Gangopadhyay, Abhijit Bhattacharyya, Goutam Manna
https://arxiv.org/abs/2506.05674

Observational Insights on DBI K-essence Models Using Machine Learning and Bayesian Analysis
We present a comparative observational data analysis of two DBI-type k-essence scalar field models for late-time cosmic acceleration: one treats dark energy with standard matter, while the other unifies dark energy and dark matter within a single scalar field, each governed by distinct non-canonical Lagrangians. The background dynamics are formulated via modified Friedmann and scalar field equations, constrained by Pantheon+ SNe Ia, Hubble, and BAO data, using Bayesian inference (NUTS in NumPyr…

Box-Constrained Softmax Function and Its Application for Post-Hoc Calibration
Kyohei Atarashi, Satoshi Oyama, Hiromi Arai, Hisashi Kashima
https://arxiv.org/abs/2506.10572

Box-Constrained Softmax Function and Its Application for Post-Hoc Calibration
Controlling the output probabilities of softmax-based models is a common problem in modern machine learning. Although the $\mathrm{Softmax}$ function provides soft control via its temperature parameter, it lacks the ability to enforce hard constraints, such as box constraints, on output probabilities, which can be critical in certain applications requiring reliable and trustworthy models. In this work, we propose the box-constrained softmax ($\mathrm{BCSoftmax}$) function, a novel generalizatio…

Accelerating Newton-Schulz Iteration for Orthogonalization via Chebyshev-type Polynomials
Ekaterina Grishina, Matvey Smirnov, Maxim Rakhuba
https://arxiv.org/abs/2506.10935

Accelerating Newton-Schulz Iteration for Orthogonalization via Chebyshev-type Polynomials
The problem of computing optimal orthogonal approximation to a given matrix has attracted growing interest in machine learning. Notable applications include the recent Muon optimizer or Riemannian optimization on the Stiefel manifold. Among existing approaches, the Newton-Schulz iteration has emerged as a particularly effective solution, as it relies solely on matrix multiplications and thus achieves high computational efficiency on GPU hardware. Despite its efficiency, the method has inherent …

Trustworthiness Preservation by Copies of Machine Learning Systems
Leonardo Ceragioli, Giuseppe Primiero
https://arxiv.org/abs/2506.05203 https://

Trustworthiness Preservation by Copies of Machine Learning Systems
A common practice of ML systems development concerns the training of the same model under different data sets, and the use of the same (training and test) sets for different learning models. The first case is a desirable practice for identifying high quality and unbiased training conditions. The latter case coincides with the search for optimal models under a common dataset for training. These differently obtained systems have been considered akin to copies. In the quest for responsible AI, a l…

Private GPTs for LLM-driven testing in software development and machine learning
Jakub Jagielski, Markus Abel
https://arxiv.org/abs/2506.06509 https://

Private GPTs for LLM-driven testing in software development and machine learning
In this contribution, we examine the capability of private GPTs to automatically generate executable test code based on requirements. More specifically, we use acceptance criteria as input, formulated as part of epics, or stories, which are typically used in modern development processes. This gives product owners, or business intelligence, respectively, a way to directly produce testable criteria through the use of LLMs. We explore the quality of the so-produced tests in two ways: i) directly b…

Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning
Sayyed Farid Ahamed, Sandip Roy, Soumya Banerjee, Marc Vucovich, Kevin Choi, Abdul Rahman, Alison Hu, Edward Bowen, Sachin Shetty
https://arxiv.org/abs/2505.23791

Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning
Federated Learning (FL) is a collaborative learning framework designed to protect client data, yet it remains highly vulnerable to Intellectual Property (IP) threats. Model extraction (ME) attacks pose a significant risk to Machine Learning as a Service (MLaaS) platforms, enabling attackers to replicate confidential models by querying black-box (without internal insight) APIs. Despite FL's privacy-preserving goals, its distributed nature makes it particularly susceptible to such attacks. This p…

Adversarial Surrogate Risk Bounds for Binary Classification
Natalie S. Frank
https://arxiv.org/abs/2506.09348 https://arxiv.org/pdf/2…

Adversarial Surrogate Risk Bounds for Binary Classification
A central concern in classification is the vulnerability of machine learning models to adversarial attacks. Adversarial training is one of the most popular techniques for training robust classifiers, which involves minimizing an adversarial surrogate risk. Recent work characterized when a minimizing sequence of an adversarial surrogate risk is also a minimizing sequence of the adversarial classification risk for binary classification -- a property known as adversarial consistency. However, thes…

Beyond Scaling: Chemical Intuition as Emergent Ability of Universal Machine Learning Interatomic Potentials
Shinnosuke Hattori, Kohei Shimamura, Aiichiro Nakano, Rajiv K. Kalia, Priya Vashishta, Ken-ichi Nomura
https://arxiv.org/abs/2506.07579

Beyond Scaling: Chemical Intuition as Emergent Ability of Universal Machine Learning Interatomic Potentials
Machine Learning Interatomic Potentials (MLIPs) have successfully demonstrated scaling behavior, i.e. the power-law improvement in training performance, however the emergence of novel capabilities at scale remains unexplored. We have developed Edge-wise Emergent Decomposition (E3D) framework to investigate how an MLIP develops the ability to derive physically meaningful local representations of chemical bonds without explicit supervision. Employing an E(3)-equivariant network (Allegro) trained …

This https://arxiv.org/abs/2505.24765 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
Supervised Quantum Machine Learning (QML) represents an intersection of quantum computing and classical machine learning, aiming to use quantum resources to support model training and inference. This paper reviews recent developments in supervised QML, focusing on methods such as variational quantum circuits, quantum neural networks, and quantum kernel methods, along with hybrid quantum-classical workflows. We examine recent experimental studies that show partial indications of quantum advantag…

Machine Learning for Consistency Violation Faults Analysis
Kamal Giri, Amit Garu
https://arxiv.org/abs/2506.02002 https://arxiv.org/p…

Machine Learning for Consistency Violation Faults Analysis
Distributed systems frequently encounter consistency violation faults (cvfs), where nodes operate on outdated or inaccurate data, adversely affecting convergence and overall system performance. This study presents a machine learning-based approach for analyzing the impact of CVFs, using Dijkstra's Token Ring problem as a case study. By computing program transition ranks and their corresponding effects, the proposed method quantifies the influence of cvfs on system behavior. To address the state…

QMCTorch: Molecular Wavefunctions with Neural Components for Energy and Force Calculations
Nicolas Renaud
https://arxiv.org/abs/2506.09743 https://

QMCTorch: Molecular Wavefunctions with Neural Components for Energy and Force Calculations
In this paper, we present results obtained using QMCTorch, a modular framework for real-space Quantum Monte Carlo (QMC) simulations of small molecular systems. Built on the popular deep learning library PyTorch, QMCTorch is GPU-native and enables the integration of machine learning-inspired components into the wave function ansatz, such as neural network backflow transformations and Jastrow factors, while leveraging efficient optimization algorithms. QMCTorch interfaces with two widely used qua…

Do-PFN: In-Context Learning for Causal Effect Estimation
Jake Robertson, Arik Reuter, Siyuan Guo, Noah Hollmann, Frank Hutter, Bernhard Sch\"olkopf
https://arxiv.org/abs/2506.06039

Do-PFN: In-Context Learning for Causal Effect Estimation
Estimation of causal effects is critical to a range of scientific disciplines. Existing methods for this task either require interventional data, knowledge about the ground truth causal graph, or rely on assumptions such as unconfoundedness, restricting their applicability in real-world settings. In the domain of tabular machine learning, Prior-data fitted networks (PFNs) have achieved state-of-the-art predictive performance, having been pre-trained on synthetic data to solve tabular prediction…

This https://arxiv.org/abs/2505.13182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

This https://arxiv.org/abs/2502.03578 has been replaced.
initial toot: https://mastoxiv.page/@a…

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
Recent advances in machine learning, combined with the generation of extensive density functional theory (DFT) datasets, have enabled the development of universal machine learning interatomic potentials (uMLIPs). These models offer broad applicability across the periodic table, achieving first-principles accuracy at a fraction of the computational cost of traditional DFT calculations. In this study, we demonstrate that state-of-the-art pretrained uMLIPs can effectively replace DFT for accuratel…

[2025-06-12 Thu (UTC), 7 new articles found for stat.ML Machine Learning]
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Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models
Maja Franz, Melvin Strobl, Leonid Chaichenets, Eileen Kuehn, Achim Streit, Wolfgang Mauerer
https://arxiv.org/abs/2506.09527

Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models
The field of variational quantum algorithms, in particular quantum machine learning (QML), produced numerous theoretical and empirical insights in recent years. As variational quantum circuits (VQCs) can be represented by Fourier series that contain an exponentially large spectrum in the number of input features, hope for quantum advantage remains. Nevertheless, properties of quantum Fourier models (QFMs) are not yet fully understood, in particular how they could potentially outperform classica…

How to craft a deep reinforcement learning policy for wind farm flow control
Elie Kadoche, Pascal Bianchi, Florence Carton, Philippe Ciblat, Damien Ernst
https://arxiv.org/abs/2506.06204

How to craft a deep reinforcement learning policy for wind farm flow control
Within wind farms, wake effects between turbines can significantly reduce overall energy production. Wind farm flow control encompasses methods designed to mitigate these effects through coordinated turbine control. Wake steering, for example, consists in intentionally misaligning certain turbines with the wind to optimize airflow and increase power output. However, designing a robust wake steering controller remains challenging, and existing machine learning approaches are limited to quasi-sta…

Big Data-Driven Fraud Detection Using Machine Learning and Real-Time Stream Processing
Chen Liu, Hengyu Tang, Zhixiao Yang, Ke Zhou, Sangwhan Cha
https://arxiv.org/abs/2506.02008 …

Big Data-Driven Fraud Detection Using Machine Learning and Real-Time Stream Processing
In the age of digital finance, detecting fraudulent transactions and money laundering is critical for financial institutions. This paper presents a scalable and efficient solution using Big Data tools and machine learning models. We utilize realtime data streaming platforms like Apache Kafka and Flink, distributed processing frameworks such as Apache Spark, and cloud storage services AWS S3 and RDS. A synthetic dataset representing real-world Anti-Money Laundering (AML) challenges is employed t…

Wasserstein Distances on Quantum Structures: an Overview
Emily Beatty
https://arxiv.org/abs/2506.09794 https://arxiv.org/pdf/2506.097…

Wasserstein Distances on Quantum Structures: an Overview
The theory of optimal transport of probability measures has wide-ranging applications across a number of different fields, including concentration of measure, machine learning, Markov chains, and economics. The generalisation of optimal transport tools from probability measures to quantum states has shown great promise over the last few years, particularly in the development of the theory of Wasserstein-style distances and divergences between quantum states. Such distances have already led to a…

The Impact of Software Testing with Quantum Optimization Meets Machine Learning
Gopichand Bandarupalli
https://arxiv.org/abs/2506.02090 https://

The Impact of Software Testing with Quantum Optimization Meets Machine Learning
Modern software systems complexity challenges efficient testing, as traditional machine learning (ML) struggles with large test suites. This research presents a hybrid framework integrating Quantum Annealing with ML to optimize test case prioritization in CI/CD pipelines. Leveraging quantum optimization, it achieves a 25 percent increase in defect detection efficiency and a 30 percent reduction in test execution time versus classical ML, validated on the Defects4J dataset. A simulated CI/CD env…

LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting
Yilin Zhuang, Karthik Duraisamy
https://arxiv.org/abs/2506.09193 https://…

LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting
Accurate probabilistic weather forecasting demands both high accuracy and efficient uncertainty quantification, challenges that overburden both ensemble numerical weather prediction (NWP) and recent machine-learning methods. We introduce LaDCast, the first global latent-diffusion framework for medium-range ensemble forecasting, which generates hourly ensemble forecasts entirely in a learned latent space. An autoencoder compresses high-dimensional ERA5 reanalysis fields into a compact representa…

DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Kevin Han, Bowen Deng, Amir Barati Farimani, Gerbrand Ceder
https://arxiv.org/abs/2506.02023

DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Large-scale atomistic simulations are essential to bridge computational materials and chemistry to realistic materials and drug discovery applications. In the past few years, rapid developments of machine learning interatomic potentials (MLIPs) have offered a solution to scale up quantum mechanical calculations. Parallelizing these interatomic potentials across multiple devices poses a challenging, but promising approach to further extending simulation scales to real-world applications. In this…

Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
Srikanth Thudumu, Jason Fisher, Hung Du
https://arxiv.org/abs/2505.24765

Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
Supervised Quantum Machine Learning (QML) represents an intersection of quantum computing and classical machine learning, aiming to use quantum resources to support model training and inference. This paper reviews recent developments in supervised QML, focusing on methods such as variational quantum circuits, quantum neural networks, and quantum kernel methods, along with hybrid quantum-classical workflows. We examine recent experimental studies that show partial indications of quantum advantag…

Machine Learning-Assisted Analysis of Combustion and Ignition in As-milled and Annealed Al/Zr Composite Powders
Michael R. Flickinger, Sreenivas Raguraman, Amee L. Polk, Colin Goodman, Megan Bokhoor, Rami Knio, Michael Kruppa, Mark A. Foster, Timothy P. Weihs
https://arxiv.org/abs/2506.06364…

Machine Learning-Assisted Analysis of Combustion and Ignition in As-milled and Annealed Al/Zr Composite Powders
Micron-scale metal-based composite powders are promising for energetic applications due to their tailored ignition and combustion properties. In particular, ball-milled Al/Zr composites exhibit lower ignition thresholds than pure aluminum, driven by exothermic intermetallic formation reactions and have demonstrated enhanced combustion properties. However, the extent to which this heat release governs ignition and combustion remains unclear, especially when progressively removed through annealin…

[2025-06-11 Wed (UTC), 6 new articles found for stat.ML Machine Learning]
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A weighted quantum ensemble of homogeneous quantum classifiers
Emiliano Tolotti, Enrico Blanzieri, Davide Pastorello
https://arxiv.org/abs/2506.07810 https…

A weighted quantum ensemble of homogeneous quantum classifiers
Ensemble methods in machine learning aim to improve prediction accuracy by combining multiple models. This is achieved by ensuring diversity among predictors to capture different data aspects. Homogeneous ensembles use identical models, achieving diversity through different data subsets, and weighted-average ensembles assign higher influence to more accurate models through a weight learning procedure. We propose a method to achieve a weighted homogeneous quantum ensemble using quantum classifie…

System-Aware Unlearning Algorithms: Use Lesser, Forget Faster
Linda Lu, Ayush Sekhari, Karthik Sridharan
https://arxiv.org/abs/2506.06073 https://

System-Aware Unlearning Algorithms: Use Lesser, Forget Faster
Machine unlearning addresses the problem of updating a machine learning model/system trained on a dataset $S$ so that the influence of a set of deletion requests $U \subseteq S$ on the unlearned model is minimized. The gold standard definition of unlearning demands that the updated model, after deletion, be nearly identical to the model obtained by retraining. This definition is designed for a worst-case attacker (one who can recover not only the unlearned model but also the remaining data samp…

Path Signatures for Feature Extraction. An Introduction to the Mathematics Underpinning an Efficient Machine Learning Technique
Stephan Sturm
https://arxiv.org/abs/2506.01815

Path Signatures for Feature Extraction. An Introduction to the Mathematics Underpinning an Efficient Machine Learning Technique
We provide an introduction to the topic of path signatures as means of feature extraction for machine learning from data streams. The article stresses the mathematical theory underlying the signature methodology, highlighting the conceptual character without plunging into the technical details of rigorous proofs. These notes are based on an introductory presentation given to students of the Research Experience for Undergraduates in Industrial Mathematics and Statistics at Worcester Polytechnic …

Superposed Parameterised Quantum Circuits
Viktoria Patapovich, Mo Kordzanganeh, Alexey Melnikov
https://arxiv.org/abs/2506.08749 https://

Superposed Parameterised Quantum Circuits
Quantum machine learning has shown promise for high-dimensional data analysis, yet many existing approaches rely on linear unitary operations and shared trainable parameters across outputs. These constraints limit expressivity and scalability relative to the multi-layered, non-linear architectures of classical deep networks. We introduce superposed parameterised quantum circuits to overcome these limitations. By combining flip-flop quantum random-access memory with repeat-until-success protocol…

This https://arxiv.org/abs/2205.10538 has been replaced.
link: https://scholar.google.com/scholar?q=a

Automated machine learning: AI-driven decision making in business analytics
The realization that AI-driven decision-making is indispensable in today's fast-paced and ultra-competitive marketplace has raised interest in industrial machine learning (ML) applications significantly. The current demand for analytics experts vastly exceeds the supply. One solution to this problem is to increase the user-friendliness of ML frameworks to make them more accessible for the non-expert. Automated machine learning (AutoML) is an attempt to solve the problem of expertise by providin…

This https://arxiv.org/abs/2505.17226 has been replaced.
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Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation
Federated Learning (FL) enables collaborative machine learning across decentralized data sources without sharing raw data. It offers a promising approach to privacy-preserving AI. However, FL remains vulnerable to adversarial threats from malicious participants, referred to as Byzantine clients, who can send misleading updates to corrupt the global model. Traditional aggregation methods, such as simple averaging, are not robust to such attacks. More resilient approaches, like the Krum algorithm…