
2025-06-13 21:45:24
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 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
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
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
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/
Black hole/quantum machine learning correspondence
Jae-Weon Lee, Zae Young Kim
https://arxiv.org/abs/2506.09678 https://arxiv.org/pdf…
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
Learning Safe Control via On-the-Fly Bandit Exploration
Alexandre Capone, Ryan Cosner, Aaaron Ames, Sandra Hirche
https://arxiv.org/abs/2506.10279 https://…
Evaluation of Machine Learning Models in Student Academic Performance Prediction
A. G. R. Sandeepa, Sanka Mohottala
https://arxiv.org/abs/2506.08047 https:…
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
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
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://
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
Scientific machine learning in Hydrology: a unified perspective
Adoubi Vincent De Paul Adombi
https://arxiv.org/abs/2506.06308 https://
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
Evasion Attacks Against Bayesian Predictive Models
Pablo G. Arce, Roi Naveiro, David R\'ios Insua
https://arxiv.org/abs/2506.09640 https://
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 Left-Right Breaking from Gravitational Waves
William Searle, Csaba Bal\'azs, Yang Xiao, Yang Zhang
https://arxiv.org/abs/2506.09319 ht…
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
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
Introduction to Predictive Coding Networks for Machine Learning
Mikko Stenlund
https://arxiv.org/abs/2506.06332 https://arxiv.org/pdf…
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
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
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
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
Local surrogates for quantum machine learning
Sreeraj Rajindran Nair, Christopher Ferrie
https://arxiv.org/abs/2506.09425 https://arx…
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
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
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
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
This https://arxiv.org/abs/2410.08850 has been replaced.
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midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
Ryoichi Asashiba, Reiji Kozuma, Hirokazu Iwasawa
https://arxiv.org/abs/2506.08338
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
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
Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC
CMS Collaboration
https://arxiv.org/abs/2506.08826
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
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
Replaced article(s) found for cs.DL. https://arxiv.org/list/cs.DL/new/
[1/1]:
Forecasting high-impact research topics via machine learning on evolving knowledge graphs
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:
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
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
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
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
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
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…
[2025-06-13 Fri (UTC), 10 new articles found for stat.ML Machine Learning]
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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
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
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
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 …
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
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…
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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
Employing Discrete Fourier Transform in Representational Learning
Raoof HojatJalali, Edmondo Trentin
https://arxiv.org/abs/2506.06765 https://
Enhanced randomized Douglas-Rachford method: Improved probabilities and adaptive momentum
Liqi Guo, Ruike Xiang, Deren Han, Jiaxin Xie
https://arxiv.org/abs/2506.10261
Flexible and Efficient Drift Detection without Labels
Nelvin Tan, Yu-Ching Shih, Dong Yang, Amol Salunkhe
https://arxiv.org/abs/2506.08734 https://
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
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
The impact of extracurricular education on socioeconomic mobility in Japan: an application of causal machine learning
Yang Qiang
https://arxiv.org/abs/2506.07421
This https://arxiv.org/abs/2506.03780 has been replaced.
link: https://scholar.google.com/scholar?q=a
Situated Bayes -- Feminist and Pluriversal Perspectives on Bayesian Knowledge
Juni Schindler, Goda Klumbyt\.e, Matthew Fuller
https://arxiv.org/abs/2506.09472
This https://arxiv.org/abs/2502.03578 has been replaced.
initial toot: https://mastoxiv.page/@a…
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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
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
Accelerating Newton-Schulz Iteration for Orthogonalization via Chebyshev-type Polynomials
Ekaterina Grishina, Matvey Smirnov, Maxim Rakhuba
https://arxiv.org/abs/2506.10935
Trustworthiness Preservation by Copies of Machine Learning Systems
Leonardo Ceragioli, Giuseppe Primiero
https://arxiv.org/abs/2506.05203 https://
Private GPTs for LLM-driven testing in software development and machine learning
Jakub Jagielski, Markus Abel
https://arxiv.org/abs/2506.06509 https://
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
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
Adversarial Surrogate Risk Bounds for Binary Classification
Natalie S. Frank
https://arxiv.org/abs/2506.09348 https://arxiv.org/pdf/2…
This https://arxiv.org/abs/2505.24765 has been replaced.
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Machine Learning for Consistency Violation Faults Analysis
Kamal Giri, Amit Garu
https://arxiv.org/abs/2506.02002 https://arxiv.org/p…
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
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
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[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
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
Wasserstein Distances on Quantum Structures: an Overview
Emily Beatty
https://arxiv.org/abs/2506.09794 https://arxiv.org/pdf/2506.097…
The Impact of Software Testing with Quantum Optimization Meets Machine Learning
Gopichand Bandarupalli
https://arxiv.org/abs/2506.02090 https://
LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting
Yilin Zhuang, Karthik Duraisamy
https://arxiv.org/abs/2506.09193 https://…
Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
Srikanth Thudumu, Jason Fisher, Hung Du
https://arxiv.org/abs/2505.24765
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…
[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…
System-Aware Unlearning Algorithms: Use Lesser, Forget Faster
Linda Lu, Ayush Sekhari, Karthik Sridharan
https://arxiv.org/abs/2506.06073 https://
Superposed Parameterised Quantum Circuits
Viktoria Patapovich, Mo Kordzanganeh, Alexey Melnikov
https://arxiv.org/abs/2506.08749 https://
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Path Signatures for Feature Extraction. An Introduction to the Mathematics Underpinning an Efficient Machine Learning Technique
Stephan Sturm
https://arxiv.org/abs/2506.01815
This https://arxiv.org/abs/2205.09337 has been replaced.
link: https://scholar.google.com/scholar?q=a