Tootfinder

from my link log —
The HP-35.
http://codex99.com/design/the-hp35.html
saved 2020-12-13 https://dotat.at/:/F9KEM.html

🚴 Benchmarking On-Device Machine Learning on Apple Silicon with MLX
#apple #hardware

Benchmarking On-Device Machine Learning on Apple Silicon with MLX
The recent widespread adoption of Large Language Models (LLMs) and machine learning in general has sparked research interest in exploring the possibilities of deploying these models on smaller devices such as laptops and mobile phones. This creates a need for frameworks and approaches that are capable of taking advantage of on-device hardware. The MLX framework was created to address this need. It is a framework optimized for machine learning (ML) computations on Apple silicon devices, facilita…

🇺🇦 #NowPlaying on KEXP's #90TEEN
Molina:
🎵 Organs
#Molina
https://corpusmolina.bandcamp.com/track/organs-feat-ml-buch
https://open.spotify.com/track/3XrEOFbHz26fgR8UTmPxmP

What’s disgusting is speaking to a Palestinian person in Gaza this way.
Maybe Mastodon server admins should check for basic humanity before allowing people on. Unless that’s not a necessary prerequisite for membership in your community, of course. https://lemmy.ca/comment/20336687

Мне очень жаль терять мой стрик в Duolingo... Но введение энергии, которая тратится на каждое выполненное задание в уроке, вместо сердец, которые тратятся только когда ты ошибся — это прямо красная линия для меня. За безошибочный урок я получил -5 энергии.
Пришёл момент когда надо закончить эти абьюзивные отношения с зелёной птицей
#duolingo

Pangram Labs: ~21% of the 75,800 peer reviews submitted for ICLR 2026, a major ML conference, were fully AI-generated, and 50% contained signs of AI use (Miryam Naddaf/Nature)
https://www.nature.com/articles/d41586-025-03506-6

Major AI conference flooded with peer reviews written fully by AI
Controversy has erupted after 21% of manuscript reviews for an international AI conference were found to be generated by artificial intelligence.

RE: #Merz during visit in

ML-KEM Mythbusting
"There have been some recent concerns about ML-KEM, NIST’s standard for encryption with Post-Quantum Cryptography, related standards of the IETF, and lots of conspiracy theories about malicious actors subverting the standardization process. "
#pqc #pqcrypto

Warum meine Großmutter zurück in die Stadt zog und ich mein Nest nicht auf dem Land bauen will: kurze Wege und mehr Menschen heißt auch leichter sozialer Kontakt.
https://loma.ml/display/373ebf56-c2de9553-e191b82d366f91de
Ich finde die Initiative vom Land SH dennoc…

I finally got Link Previews in Firefox today. I disabled the ‘AI’ feature and then disabled link previews altogether as I played with it.
But the setting disappeared completely, so I couldn’t turn it on again.
If this happens to you:
about:config − set browser.ml.linkPreview.enabled to true

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Learning to sample fibers for goodness-of-fit testing
Ivan Gvozdanovi\'c, Sonja Petrovi\'c

I'm installing and tweaking #Immich, a really neat "Google Photos" like app that runs locally. It uses ML to do face detection and other neat features. I've been tuning my server and needed a better way to measure performance, so I vibe wrote a helper script. If you run Immich, this might be useful to you:

GitHub - rubix1138/immich_eta: Script that queries the Immich API and calculates and ETA
Script that queries the Immich API and calculates and ETA - rubix1138/immich_eta

I so hope this blows up in his face spectacularly.
Note that I do not believe that any LLM can become as skilled as the least skilled #InfoSec professional using conventional tools, so I’m quite confident this will not reach any useful goal.
I’m saying he needs to learn a lesson, and it would be great if it were useful for others as well. @…

SPAM's Carbon Footprint @Home (2017) - The carbon cost of fending off SPAM (and other networked malice) at home... - https://m.earth.org.uk/SPAM-and-CO2.html

SPAMs Carbon Footprint @Home (2017)
The carbon cost of fending off SPAM (and other networked malice) at home...

60 years ago #OTD #comet #IkeyaSeki reached perihelion, -10 mag., was visible in daylight and showed a spectacular tail in the following weeks: https://groups.io/g/comets-ml/message/32770 and https://www.astronomy.com/today-in-the-history-of-astronomy/sept-18-1965-discovery-of-comet-ikeya-seki/ and https://noirlab.edu/public/images/noao-01854/

from my link log —
A brief history of NSA backdoors. (2013)
https://www.ethanheilman.com/x/12/index.html
saved 2025-11-29 https://d…

An online service I’m subscribed to emailed me to say that I would be placed on the “Pro Legacy” plan at the same price of $29 annually in December.
But if upgrade to their $60/year Pro plan, I get access to AI/ML “innovations”. Uh, no, thank you!
#AI #ML

Übermorgen bei unseren Nachbarn:
#UniGöttingen

A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN
Md Habibur Rahman, Md Sharif Hossen, Nathan H. Stephenson, Vijay K. Shah, Aloizio Da Silva
https://arxiv.org/abs/2510.09706

A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN
The open radio access network (O-RAN) enables modular, intelligent, and programmable 5G network architectures through the adoption of software-defined networking, network function virtualization, and implementation of standardized open interfaces. However, one of the security concerns for O-RAN, which can severely undermine network performance, is jamming attacks. This paper presents SAJD- a self-adaptive jammer detection framework that autonomously detects jamming attacks in AI/ML framework-in…

Software Defect Prediction using Autoencoder Transformer Model
Seshu Barma, Mohanakrishnan Hariharan, Satish Arvapalli
https://arxiv.org/abs/2510.10840 https://

Software Defect Prediction using Autoencoder Transformer Model
An AI-ML-powered quality engineering approach uses AI-ML to enhance software quality assessments by predicting defects. Existing ML models struggle with noisy data types, imbalances, pattern recognition, feature extraction, and generalization. To address these challenges, we develop a new model, Adaptive Differential Evolution (ADE) based Quantum Variational Autoencoder-Transformer (QVAET) Model (ADE-QVAET). ADE combines with QVAET to obtain high-dimensional latent features and maintain sequent…

Another ML(#MarxismLeninism) blocked me when I posted this: https://social.linux.pizza/@midtsveen/115581081054226799
Love it or hate it, we can disagree on topics …

Marked safe from AI in Firefox 144 on Linux!
(about:config is your friend: search for ".ml.")
https://www.mozilla.org/en-US/firefox/144.0/whatsnew/?oldversion=143.0.4

Dear Reader,

Today I'm delighted to announce a Take Control book about a fantastic new Mac email app called #MailMaven. Developed by our friends at SmallCubed (best known for their popular MailSuite plugin for Apple Mail), MailMaven is a powerful and flexible alternative to apps like Apple Mail, Outlook, and Thunderbird.
The Manual is free from :

Model ML, which aims to use AI agents to automate grunt work done by investment bankers like making pitch decks, raised $75M, after raising $12M earlier in 2025 (Aisha S Gani/Bloomberg)
https://www.bloomberg.com/news/articles/20

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Replicable Clustering
Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou

Code Smell Detection via Pearson Correlation and ML Hyperparameter Optimization
Moinuddin Muhammad Imtiaz Bhuiyan, Kazi Ekramul Hoque, Rakibul Islam, Md. Mahbubur Rahman Tusher, Najmul Hassan, Yoichi Tomioka, Satoshi Nishimura, Jungpil Shin, Abu Saleh Musa Miah
https://arxiv.org/abs/2510.05835

Code Smell Detection via Pearson Correlation and ML Hyperparameter Optimization
This study addresses the challenge of detecting code smells in large-scale software systems using machine learning (ML). Traditional detection methods often suffer from low accuracy and poor generalization across different datasets. To overcome these issues, we propose a machine learning-based model that automatically and accurately identifies code smells, offering a scalable solution for software quality analysis. The novelty of our approach lies in the use of eight diverse ML algorithms, incl…

Там вышла 4я часть иски по финалке
Сначала думал, что опять Ютуб древние архивы поднял, а потом увидел время публикации... Я поражен до глубины души
https://youtu.be/X2SDwyldfzU

Ethical Considerations Around Machine Learning-Engaged Online Participatory Research - poster from Zooniverse community at #FF2025 https://zenodo.org/records/17779992

Believe it or not, I've never actually submitted an academic paper for peer-reviewed publication before, and I'm a lil nail-bitey.
Is there anyone here who's active in ACL (the Association for Computational Linguistics) who wants to swap papers with me for a deep critique of methods/code/conclusions? I have a fun classic ML model that will entertain you, I swear.

Understanding Exoplanet Habitability: A Bayesian ML Framework for Predicting Atmospheric Absorption Spectra
Vasuda Trehan, Kevin H. Knuth, M. J. Way
https://arxiv.org/abs/2510.08766

Understanding Exoplanet Habitability: A Bayesian ML Framework for Predicting Atmospheric Absorption Spectra
The evolution of space technology in recent years, fueled by advancements in computing such as Artificial Intelligence (AI) and machine learning (ML), has profoundly transformed our capacity to explore the cosmos. Missions like the James Webb Space Telescope (JWST) have made information about distant objects more easily accessible, resulting in extensive amounts of valuable data. As part of this work-in-progress study, we are working to create an atmospheric absorption spectrum prediction model…

The totality of the evidence supports the thesis that displacement of long-established dietary patterns by ultra-processed foods is a key driver of the escalating global burden of multiple diet-related chronic diseases.
Monteiro CA, Louzada ML, Steele-Martinez E, Cannon G, Andrade GC, Baker P, et al. (2025): Ultra-processed foods and human health: the main thesis and the evidence. The Lancet 0. DOI: 10.1016/S0140-6736(25)01565-X

The {purrr} package is a powerfull way to replace loops. The {furrr} package takes this approach one step further by parallel execution: #rstats

Apply Mapping Functions in Parallel using Futures
Implementations of the family of map() functions from purrr that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.

To Ask or Not to Ask: Learning to Require Human Feedback
Andrea Pugnana, Giovanni De Toni, Cesare Barbera, Roberto Pellungrini, Bruno Lepri, Andrea Passerini
https://arxiv.org/abs/2510.08314

To Ask or Not to Ask: Learning to Require Human Feedback
Developing decision-support systems that complement human performance in classification tasks remains an open challenge. A popular approach, Learning to Defer (LtD), allows a Machine Learning (ML) model to pass difficult cases to a human expert. However, LtD treats humans and ML models as mutually exclusive decision-makers, restricting the expert contribution to mere predictions. To address this limitation, we propose Learning to Ask (LtA), a new framework that handles both when and how to inco…

I can't hide that AMD cosplaying as Nvidia was a nasty surprise to me. But I am just a lowly gamer. Mad respect to the Linux hackers for knowing that corporations are not their friends, and - respectfully to AMD - bringing the goods only to Linux 🙂 ->
Modder gets FSR Redstone ML-powered Frame Generation running on RDNA 3 GPUs - VideoCardz.com

God, can someone please finally take #Firefox out of the hands of #Mozilla and their damn "AI" bros?
When they started adding all of this nonsense I went into my about:config to explicitly set "browser.ml.enable" to "false", but apparently even doesn't save you, as the new "features" will just not give a damn. 🫠

@… , we hadden het laatst over kaasfondue van schaap, hier heeft mijn websuper ineens Kaasfondue van geitenkaas - Kant en Klaar - Hofweb - de boerderij ken ik, die is, ondanks de suffe naam, goed. Ze doen er wel te weinig wijn in.

Computations and ML for surjective rational maps
Ilya Karzhemanov
https://arxiv.org/abs/2510.08093 https://arxiv.org/pdf/2510.08093

Computations and ML for surjective rational maps
The present note studies \emph{surjective rational endomorphisms} $f: \mathbb{P}^2 \dashrightarrow \mathbb{P}^2$ with \emph{cubic} terms and the indeterminacy locus $I_f \ne \emptyset$. We develop an experimental approach, based on some Python programming and Machine Learning, towards the classification of such maps; a couple of new explicit $f$ is constructed in this way. We also prove (via pure projective geometry) that a general non-regular cubic endomorphism $f$ of $\mathbb{P}^2$ is surject…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Learning Shared Representations from Unpaired Data
Amitai Yacobi, Nir Ben-Ari, Ronen Talmon, Uri Shaham

Wireless Channel Modeling for Machine Learning -- A Critical View on Standardized Channel Models
Benedikt B\"ock, Amar Kasibovic, Wolfgang Utschick
https://arxiv.org/abs/2510.12279

Wireless Channel Modeling for Machine Learning -- A Critical View on Standardized Channel Models
Standardized (link-level) channel models such as the 3GPP TDL and CDL models are frequently used to evaluate machine learning (ML)-based physical-layer methods. However, in this work, we argue that a link-level perspective incorporates limiting assumptions, causing unwanted distributional shifts or necessitating impractical online training. An additional drawback is that this perspective leads to (near-)Gaussian channel characteristics. Thus, ML-based models, trained on link-level channel data,…

Cross-correlation of Luminous Red Galaxies with ML-selected AGN in HSC-SSP III: HOD Parameters for Type I and Type II Quasars
Rodrigo C\'ordova Rosado, Andy D. Goulding, Jenny E. Greene, Nickolas Kokron, Andrina Nicola, Michael A. Strauss, Ryan C. Hickox
https://arxiv.org/abs/2510.11780

Cross-correlation of Luminous Red Galaxies with ML-selected AGN in HSC-SSP III: HOD Parameters for Type I and Type II Quasars
Understanding the dark matter (DM) halo environment in which galaxies that host active galactic nuclei (AGN) reside is a window into the nature of supermassive black hole (SMBH) accretion. We apply halo occupation distribution (HOD) modeling tools to interpret the angular cross-correlation functions between $1.5\times10^6$ luminous red galaxies (LRGs) and our $\sim28,500$ Hyper Suprime-Cam + Wide-field Infrared Survey Explorer-selected (and $L_{6 μm}$-limited) AGN to infer the halo properties …

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Lukas Krodinger, Altin Hajdari, Stephan Lukasczyk, Gordon Fraser
https://arxiv.org/abs/2510.09108 https://

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Machine learning (ML) libraries such as PyTorch and TensorFlow are essential for a wide range of modern applications. Ensuring the correctness of ML libraries through testing is crucial. However, ML APIs often impose strict input constraints involving complex data structures such as tensors. Automated test generation tools such as Pynguin are not aware of these constraints and often create non-compliant inputs. This leads to early test failures and limited code coverage. Prior work has investig…

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs
Yifan Zhao, Egan Johnson, Prasanth Chatarasi, Vikram Adve, Sasa Misailovic
https://arxiv.org/abs/2510.08726

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs
Operator fusion has become a key optimization for deep learning, which combines multiple deep learning operators to improve data reuse and reduce global memory transfers. However, existing tensor compilers struggle to fuse complex reduction computations involving loop-carried dependencies, such as attention mechanisms. The paper introduces Neptune, a tensor compiler for advanced operator fusion for sequences of reduction operators. Neptune presents a new approach for advanced operator fusion,…

LED Lighting: Mini Reviews - Real-world testing! - https://m.earth.org.uk/LED-lighting.html

LED Lighting: Mini Reviews
Real-world testing!

Automated Research Article Classification and Recommendation Using NLP and ML
Shadikur Rahman, Hasibul Karim Shanto, Umme Ayman Koana, Syed Muhammad Danish
https://arxiv.org/abs/2510.05495

Automated Research Article Classification and Recommendation Using NLP and ML
In the digital era, the exponential growth of scientific publications has made it increasingly difficult for researchers to efficiently identify and access relevant work. This paper presents an automated framework for research article classification and recommendation that leverages Natural Language Processing (NLP) techniques and machine learning. Using a large-scale arXiv.org dataset spanning more than three decades, we evaluate multiple feature extraction approaches (TF--IDF, Count Vectorize…

Sensitivity Analysis for Causal ML: A Use Case at Booking.com
Philipp Bach, Victor Chernozhukov, Carlos Cinelli, Lin Jia, Sven Klaassen, Nils Skotara, Martin Spindler
https://arxiv.org/abs/2510.09109

Sensitivity Analysis for Causal ML: A Use Case at Booking.com
Causal Machine Learning has emerged as a powerful tool for flexibly estimating causal effects from observational data in both industry and academia. However, causal inference from observational data relies on untestable assumptions about the data-generating process, such as the absence of unobserved confounders. When these assumptions are violated, causal effect estimates may become biased, undermining the validity of research findings. In these contexts, sensitivity analysis plays a crucial ro…

About how AI tools can be useful.
#ai #coding

Claude Code Can Debug Low-level Cryptography
Surprisingly (to me) Claude Code debugged my new ML-DSA implementation faster than I would have, finding the non-obvious low-level issue that was making Verify fail.

das #nvidia

First the nucleus of #comet SWAN split - https://skyweek.wordpress.com/2025/11/02/allgemeines-live-blog-ab-dem-2-november-2025/#Nov06 ("Der Kern des Kometen C/2025 R2 (SWAN) ist zerbrochen ...") - and now C/2025 K1 (ATLAS) has suffered the same fate: a 2-meter-telescope image from https://www.astronomerstelegram.org/?read=17487 ("Multiple Fragmentation Observed"), also https://groups.io/g/comets-ml/topic/116216567 (several amateur observations) and https://www.astronomerstelegram.org/?read=17488 (Hubble detections of more fragments, sadly no pictures included).

from my link log —
psychec: ML-style type inference for C.
https://github.com/ltcmelo/psychec
saved 2019-09-09 https://dotat.at/:/GN6G4.html

GitHub - ltcmelo/psychec: A compiler frontend for the C programming language
A compiler frontend for the C programming language - ltcmelo/psychec

Tracing Multilingual Knowledge Acquisition Dynamics in Domain Adaptation: A Case Study of English-Japanese Biomedical Adaptation
Xin Zhao, Naoki Yoshinaga, Yuma Tsuta, Akiko Aizawa
https://arxiv.org/abs/2510.12115

Tracing Multilingual Knowledge Acquisition Dynamics in Domain Adaptation: A Case Study of English-Japanese Biomedical Adaptation
Multilingual domain adaptation (ML-DA) is widely used to learn new domain knowledge across languages into large language models (LLMs). Although many methods have been proposed to improve domain adaptation, the mechanisms of multilingual knowledge acquisition, how domain knowledge is learned within a language and transferred across languages, remain underexplored. This gap leads to suboptimal performance, particularly in low-resource settings. This work examines the learning dynamics of LLMs du…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Sparse Robust Classification via the Kernel Mean
Brendan van Rooyen, Aditya Krishna Menon, Robert C. Williamson

Robust ML-based Detection of Conventional, LLM-Generated, and Adversarial Phishing Emails Using Advanced Text Preprocessing
Deeksha Hareesha Kulal, Chidozie Princewill Arannonu, Afsah Anwar, Nidhi Rastogi, Quamar Niyaz
https://arxiv.org/abs/2510.11915

Robust ML-based Detection of Conventional, LLM-Generated, and Adversarial Phishing Emails Using Advanced Text Preprocessing
Phishing remains a critical cybersecurity threat, especially with the advent of large language models (LLMs) capable of generating highly convincing malicious content. Unlike earlier phishing attempts which are identifiable by grammatical errors, misspellings, incorrect phrasing, and inconsistent formatting, LLM generated emails are grammatically sound, contextually relevant, and linguistically natural. These advancements make phishing emails increasingly difficult to distinguish from legitimat…

Comparison of Parametric versus Machine-learning Multiple Imputation in Clinical Trials with Missing Continuous Outcomes
Mia S. Tackney, Jonathan W. Bartlett, Elizabeth Williamson, Kim May Lee
https://arxiv.org/abs/2510.03512

Comparison of Parametric versus Machine-learning Multiple Imputation in Clinical Trials with Missing Continuous Outcomes
The use of flexible machine-learning (ML) models to generate imputations of missing data within the framework of Multiple Imputation (MI) has recently gained traction, particularly in observational settings. For randomised controlled trials (RCTs), it is unclear whether ML approaches to MI provide valid inference, and whether they outperform parametric MI approaches under complex data generating mechanisms. We conducted two simulations in RCT settings that have incomplete continuous outcomes bu…

Soumith Chintala, who co-created the PyTorch ML framework at Meta and left the company this month, has joined Mira Murati's Thinking Machines Lab (Pranav Dixit/Business Insider)
https://www.businessinsider.com/meta-soumith-chinta…

A top Meta AI researcher has joined Mira Murati's hot new startup, Thinking Machines Lab
Thinking Machines Lab, led by Mira Murati, hires Meta AI veteran Soumith Chintala as it rapidly expands its team and prepares for major funding.

In-pixel integration of signal processing and AI/ML based data filtering for particle tracking detectors
Benjamin Parpillon, Anthony Badea, Danush Shekar, Christian Gingu, Giuseppe Di Guglielmo, Tom Deline, Adam Quinn, Michele Ronchi, Benjamin Weiss, Jennet Dickinson, Jieun Yoo, Corrinne Mills, Daniel Abadjiev, Eric You, Mira Littmann, Karri DiPetrillo, Arghya Ranjan Das, Mia Liu, David Jiang, Mark S. Neubauer, Morris Swartz, Petar Maksimovic, Alice Bean, Chinar Syal, Doug Berry, Nhan …

In-pixel integration of signal processing and AI/ML based data filtering for particle tracking detectors
We present the first physical realization of in-pixel signal processing with integrated AI-based data filtering for particle tracking detectors. Building on prior work that demonstrated a physics-motivated edge-AI algorithm suitable for ASIC implementation, this work marks a significant milestone toward intelligent silicon trackers. Our prototype readout chip performs real-time data reduction at the sensor level while meeting stringent requirements on power, area, and latency. The chip is taped…

Interpreting anomaly detection of SDSS spectra
Edgar Ortiz Manrique, M\'ed\'eric Boquien
https://arxiv.org/abs/2510.05235 https://arxiv.org/pdf/251…

Interpreting anomaly detection of SDSS spectra
The increasing use of ML in astronomy introduces important questions about interpretability. Due to their complexity and non-linear nature, it can be challenging to understand their decision-making process. While these models can effectively identify unusual spectra, interpreting the physical nature of the flagged outliers remains a major challenge. We aim to bridge the gap between anomaly detection and physical understanding by combining deep learning with interpretable ML (iML) techniques to …

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[3/3]:
- An Eulerian Perspective on Straight-Line Sampling
Panos Tsimpos, Youssef Marzouk

Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model
Helge Heuer, Tom Beucler, Mierk Schwabe, Julien Savre, Manuel Schlund, Veronika Eyring
https://arxiv.org/abs/2510.08107

Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model
Persistent systematic errors in Earth system models (ESMs) arise from difficulties in representing the full diversity of subgrid, multiscale atmospheric convection and turbulence. Machine learning (ML) parameterizations trained on short high-resolution simulations show strong potential to reduce these errors. However, stable long-term atmospheric simulations with hybrid (physics + ML) ESMs remain difficult, as neural networks (NNs) trained offline often destabilize online runs. Training convect…

A Living Review Pipeline for AI/ML Applications in Accelerator Physics
Adnan Ghribi
https://arxiv.org/abs/2510.09376 https://arxiv.org/pdf/2510.09376

A Living Review Pipeline for AI/ML Applications in Accelerator Physics
We present an open-source pipeline for generating a \emph{living review} of artificial intelligence (AI) and machine learning (ML) applications in accelerator physics and technologies. Traditional review articles provide static snapshots that are quickly outdated by the rapid pace of research. The presented system automatically harvests publications from multiple bibliographic sources (arXiv, InspireHEP, HAL, OpenAlex, Crossref, and Springer), deduplicates entries, applies semantic filtering to…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/3]:
- What Makes Looped Transformers Perform Better Than Non-Recursive Ones (Provably)
Zixuan Gong, Jiaye Teng, Yong Liu

Prismo: A Decision Support System for Privacy-Preserving ML Framework Selection
Nges Brian Njungle, Eric Jahns, Luigi Mastromauro, Edwin P. Kayang, Milan Stojkov, Michel A. Kinsy
https://arxiv.org/abs/2510.09985

Prismo: A Decision Support System for Privacy-Preserving ML Framework Selection
Machine learning has become a crucial part of our lives, with applications spanning nearly every aspect of our daily activities. However, using personal information in machine learning applications has sparked significant security and privacy concerns about user data. To address these challenges, different privacy-preserving machine learning (PPML) frameworks have been developed to protect sensitive information in machine learning applications. These frameworks generally attempt to balance desi…

Lightning talks include IIIF's AI/ML working group presented by the excellent Martin Kalfatovic https://www.conftool.org/fantastic-futures-2025/index.php?page=browseSessions&ismobile=true&form_session=26

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/3]:
- Performance of Machine Learning Methods for Gravity Inversion: Successes and Challenges
Vahid Negahdari, Shirin Samadi Bahrami, Seyed Reza Moghadasi, Mohammad Reza Razvan

On 16WW Data Collections and Graphs - Open for research #dataset - https://m.earth.org.uk/note-on-data.html

On 16WW Data Collections and Graphs
Open for research #dataset

A reference to this GitHub blog post has come at me a few different ways, eg:
https://bsky.app/profile/longtail.news/post/3m2cfqbihhq2c
But that URL is 404:

Efficient Domain Generalization in Wireless Networks with Scarce Multi-Modal Data
Minsu Kim, Walid Saad, Dour Calin
https://arxiv.org/abs/2510.04359 https://

Efficient Domain Generalization in Wireless Networks with Scarce Multi-Modal Data
In 6G wireless networks, multi-modal ML models can be leveraged to enable situation-aware network decisions in dynamic environments. However, trained ML models often fail to generalize under domain shifts when training and test data distributions are different because they often focus on modality-specific spurious features. In practical wireless systems, domain shifts occur frequently due to dynamic channel statistics, moving obstacles, or hardware configuration. Thus, there is a need for learn…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Multiparameter regularization and aggregation in the context of polynomial functional regression
Gizewski, Holzleitner, Mayer-Suess, Pereverzyev, Pereverzyev

Formal Models and Convergence Analysis for Context-Aware Security Verification
Ayush Chaudhary
https://arxiv.org/abs/2510.12440 https://arxiv.org/pdf/2510.…

Formal Models and Convergence Analysis for Context-Aware Security Verification
We present a formal framework for context-aware security verification that establishes provable guarantees for ML-enhanced adaptive systems. We introduce context-completeness - a new security property - and prove: (1) sample complexity bounds showing when adaptive verification succeeds, (2) information-theoretic limits relating context richness to detection capability, (3) convergence guarantees for ML-based payload generators, and (4) compositional soundness bounds. We further provide a formal…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Energy-Driven Steering: Reducing False Refusals in Large Language Models
Jiang, Ou, Liu, Pang, Wan, Duan, Dong, Chang, Wang, Wu, Li

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Sharp Structure-Agnostic Lower Bounds for General Functional Estimation
Jikai Jin, Vasilis Syrgkanis
https://arxiv.org/abs/2512.17341 https://mastoxiv.page/@arXiv_statML_bot/115762312049963700
- Timely Information Updating for Mobile Devices Without and With ML Advice
Yu-Pin Hsu, Yi-Hsuan Tseng
https://arxiv.org/abs/2512.17381 https://mastoxiv.page/@arXiv_csNI_bot/115762180316858485
- SWE-Bench : A Framework for the Scalable Generation of Software Engineering Benchmarks from Open...
Wang, Ramalho, Celestino, Pham, Liu, Sinha, Portillo, Osunwa, Maduekwe
https://arxiv.org/abs/2512.17419 https://mastoxiv.page/@arXiv_csSE_bot/115762487015279852
- Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing
Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi
https://arxiv.org/abs/2512.17426 https://mastoxiv.page/@arXiv_statML_bot/115762346108219997
- MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic s...
Jon Muhovi\v{c}, Janez Per\v{s}
https://arxiv.org/abs/2512.17450 https://mastoxiv.page/@arXiv_csCV_bot/115762717053353674
- When Data Quality Issues Collide: A Large-Scale Empirical Study of Co-Occurring Data Quality Issu...
Emmanuel Charleson Dapaah, Jens Grabowski
https://arxiv.org/abs/2512.17460 https://mastoxiv.page/@arXiv_csSE_bot/115762500123147574
- Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application
Olivier Jeunen, Schaun Wheeler
https://arxiv.org/abs/2512.17462 https://mastoxiv.page/@arXiv_csIR_bot/115762430673347625
- Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks
Irched Chafaa, Giacomo Bacci, Luca Sanguinetti
https://arxiv.org/abs/2512.17466 https://mastoxiv.page/@arXiv_eessSY_bot/115762336277179643
- Translating the Rashomon Effect to Sequential Decision-Making Tasks
Dennis Gross, J{\o}rn Eirik Betten, Helge Spieker
https://arxiv.org/abs/2512.17470 https://mastoxiv.page/@arXiv_csAI_bot/115762556506696539
- Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions
Atharva Awari, Nicolas Gillis, Arnaud Vandaele
https://arxiv.org/abs/2512.17473 https://mastoxiv.page/@arXiv_eessSP_bot/115762580078964235
- TwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis
Almustapha A. Wakili, Adamu Hussaini, Abubakar A. Musa, Woosub Jung, Wei Yu
https://arxiv.org/abs/2512.17488 https://mastoxiv.page/@arXiv_csCV_bot/115762726884307901
- Resource-efficient medical image classification for edge devices
Mahsa Lavaei, Zahra Abadi, Salar Beigzad, Alireza Maleki
https://arxiv.org/abs/2512.17515 https://mastoxiv.page/@arXiv_eessIV_bot/115762459510336799
- PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning i...
Brussee, Valkema, Weijer, Doeleman, Schrader, Kers
https://arxiv.org/abs/2512.17517 https://mastoxiv.page/@arXiv_csCV_bot/115762741957639051
- HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, et al.
https://arxiv.org/abs/2512.17534 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115762391350754768
- When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Sys...
Chondhekar, Murukuri, Vasani, Goyal, Badami, Rana, SN, Pandia, Katiyar, Jagadeesh, Gulati
https://arxiv.org/abs/2512.17562 https://mastoxiv.page/@arXiv_csSD_bot/115762423443170715
- Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing
Lingxiao Zhao, Haoran Zhou, Yuezhi Che, Dazhao Cheng
https://arxiv.org/abs/2512.17574 https://mastoxiv.page/@arXiv_csDC_bot/115762425409322293
- SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation i...
N. A. Adarsh Pritam, Jeba Shiney O, Sanyam Jain
https://arxiv.org/abs/2512.17585 https://mastoxiv.page/@arXiv_eessIV_bot/115762479150695610
- MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection an...
Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Asif Rahman, Olukunle Kolade, Sasidhar Kunapuli
https://arxiv.org/abs/2512.17594 https://mastoxiv.page/@arXiv_csCR_bot/115762509298207765
- Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion De...
Menna Elgabry, Ali Hamdi
https://arxiv.org/abs/2512.17630 https://mastoxiv.page/@arXiv_csCL_bot/115762575512981257
- Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Effic...
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson
https://arxiv.org/abs/2512.17659 https://mastoxiv.page/@arXiv_statML_bot/115762554519447500
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Representing Subgrid-Scale Cloud Effects in a Radiation Parameterization using Machine Learning: MLe-radiation v1.0
Katharina Hafner, Sara Shamekh, Guillaume Bertoli, Axel Lauer, Robert Pincus, Julien Savre, Veronika Eyring
https://arxiv.org/abs/2510.05963

Representing Subgrid-Scale Cloud Effects in a Radiation Parameterization using Machine Learning: MLe-radiation v1.0
Improvements of Machine Learning (ML)-based radiation emulators remain constrained by the underlying assumptions to represent horizontal and vertical subgrid-scale cloud distributions, which continue to introduce substantial uncertainties. In this study, we introduce a method to represent the impact of subgrid-scale clouds by applying ML to learn processes from high-resolution model output with a horizontal grid spacing of 5km. In global storm resolving models, clouds begin to be explicitly res…

Post-Quantum Cryptography and Quantum-Safe Security: A Comprehensive Survey
Gaurab Chhetri, Shriyank Somvanshi, Pavan Hebli, Shamyo Brotee, Subasish Das
https://arxiv.org/abs/2510.10436

Post-Quantum Cryptography and Quantum-Safe Security: A Comprehensive Survey
Post-quantum cryptography (PQC) is moving from evaluation to deployment as NIST finalizes standards for ML-KEM, ML-DSA, and SLH-DSA. This survey maps the space from foundations to practice. We first develop a taxonomy across lattice-, code-, hash-, multivariate-, isogeny-, and MPC-in-the-Head families, summarizing security assumptions, cryptanalysis, and standardization status. We then compare performance and communication costs using representative, implementation-grounded measurements, and re…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Latency-Aware Contextual Bandit: Application to Cryo-EM Data Collection
Lai Wei, Ambuj Tewari, Michael A. Cianfrocco

LED Lighting: Mini Reviews - Real-world testing! - https://m.earth.org.uk/LED-lighting.html

LED Lighting: Mini Reviews
Real-world testing!

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- DUA-D2C: Dynamic Uncertainty Aware Method for Overfitting Remediation in Deep Learning
Md. Saiful Bari Siddiqui, Md Mohaiminul Islam, Md. Golam Rabiul Alam

Prepared for the Unknown: Adapting AIOps Capacity Forecasting Models to Data Changes
Lorena Poenaru-Olaru, Wouter van 't Hof, Adrian Stando, Arkadiusz P. Trawinski, Eileen Kapel, Jan S. Rellermeyer, Luis Cruz, Arie van Deursen
https://arxiv.org/abs/2510.10320

Prepared for the Unknown: Adapting AIOps Capacity Forecasting Models to Data Changes
Capacity management is critical for software organizations to allocate resources effectively and meet operational demands. An important step in capacity management is predicting future resource needs often relies on data-driven analytics and machine learning (ML) forecasting models, which require frequent retraining to stay relevant as data evolves. Continuously retraining the forecasting models can be expensive and difficult to scale, posing a challenge for engineering teams tasked with balanc…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- An Empirical Analysis of the Laplace and Neural Tangent Kernels
Ronaldas Paulius Lencevi\v{c}ius

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- A Mixed-Methods Analysis of Repression and Mobilization in Bangladesh's July Revolution Using Mac...
Md. Saiful Bari Siddiqui, Anupam Debashis Roy

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Model-free generalized fiducial inference
Jonathan P Williams
https://…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Adaptive Reinforcement Learning for Dynamic Configuration Allocation in Pre-Production Testing
Yu Zhu

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data
Efthymios Costa, Ioanna Papatsouma, Angelos Markos

Operationalizing AI: Empirical Evidence on MLOps Practices, User Satisfaction, and Organizational Context
Stefan Pasch
https://arxiv.org/abs/2510.09968 https://

Operationalizing AI: Empirical Evidence on MLOps Practices, User Satisfaction, and Organizational Context
Organizational efforts to utilize and operationalize artificial intelligence (AI) are often accompanied by substantial challenges, including scalability, maintenance, and coordination across teams. In response, the concept of Machine Learning Operations (MLOps) has emerged as a set of best practices that integrate software engineering principles with the unique demands of managing the ML lifecycle. Yet, empirical evidence on whether and how these practices support users in developing and operat…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Mixtures of Gaussian Process Experts with SMC$^2$
Teemu H\"ark\"onen, Sara Wade, Kody Law, Lassi Roininen

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[3/3]:
- Domain Generalization: A Tale of Two ERMs
Yilun Zhu, Naihao Deng, Naichen Shi, Aditya Gangrade, Clayton Scott

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/3]:
- Group Policy Gradient
Junhua Chen, Zixi Zhang, Hantao Zhong, Rika Antonova

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/3]:
- Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies
Zheng, Venkatasubramanian, Li, Braverman, Ke, Hou, Jin, Agrawal

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Gradient Descent with Large Step Sizes: Chaos and Fractal Convergence Region
Shuang Liang, Guido Mont\'ufar

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Discrimination in machine learning algorithms
Roberta Pappad\`a, Francesco Pauli

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Orthogonal Procrustes problem preserves correlations in synthetic data
Oussama Ounissi, Nicklas J\"averg\r{a}rd, Adrian Muntean

Comparison of Fully Homomorphic Encryption and Garbled Circuit Techniques in Privacy-Preserving Machine Learning Inference
Kalyan Cheerla (University of North Texas), Lotfi Ben Othmane (University of North Texas), Kirill Morozov (University of North Texas)
https://arxiv.org/abs/2510.07457

Comparison of Fully Homomorphic Encryption and Garbled Circuit Techniques in Privacy-Preserving Machine Learning Inference
Machine Learning (ML) is making its way into fields such as healthcare, finance, and Natural Language Processing (NLP), and concerns over data privacy and model confidentiality continue to grow. Privacy-preserving Machine Learning (PPML) addresses this challenge by enabling inference on private data without revealing sensitive inputs or proprietary models. Leveraging Secure Computation techniques from Cryptography, two widely studied approaches in this domain are Fully Homomorphic Encryption (F…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Provably Scalable Black-Box Variational Inference with Structured Variational Families
Joohwan Ko, Kyurae Kim, Woo Chang Kim, Jacob R. Gardner

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Group Averaging for Physics Applications: Accuracy Improvements at Zero Training Cost
Valentino F. Foit, David W. Hogg, Soledad Villar

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Differentiable, Bit-shifting, and Scalable Quantization without training neural network from scratch
Zia Badar

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Dataset-Free Weight-Initialization on Restricted Boltzmann Machine
Muneki Yasuda, Ryosuke Maeno, Chako Takahashi

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- A metrological framework for uncertainty evaluation in machine learning classification models
Samuel Bilson, Maurice Cox, Anna Pustogvar, Andrew Thompson

[2025-10-15 Wed (UTC), 15 new articles found for stat.ML Machine Learning]
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[2025-10-13 Mon (UTC), 8 new articles found for stat.ML Machine Learning]
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[2025-10-10 Fri (UTC), 14 new articles found for stat.ML Machine Learning]
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[2025-10-09 Thu (UTC), 10 new articles found for stat.ML Machine Learning]
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[2025-10-08 Wed (UTC), 7 new articles found for stat.ML Machine Learning]
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