Tootfinder

ML 6 Moonlander: Autonomer 32-Tonnen-Bulldozer fährt vollelektrisch
Der elektrische ML 6 Moonlander soll herkömmlich betriebene Bulldozer ersetzen und die Umwelt schonen. Das funktioniert auch autonom und ferngesteuert.

@… hello! What plays as frequency source in your power backup setup with the battery of you EV as primary energy source?
I'm no electrician. From my understanding we have something like diesel generator that does not work in sync with the grid, and DC/AC converters that we want to be able to run in sync with the grid and off the grid. And as far a…

Enhancing the Merger Simulation Toolkit with ML/AI
Harold D. Chiang, Jack Collison, Lorenzo Magnolfi, Christopher Sullivan
https://arxiv.org/abs/2506.05225

Enhancing the Merger Simulation Toolkit with ML/AI
This paper develops a flexible approach to predict the price effects of horizontal mergers using ML/AI methods. While standard merger simulation techniques rely on restrictive assumptions about firm conduct, we propose a data-driven framework that relaxes these constraints when rich market data are available. We develop and identify a flexible nonparametric model of supply that nests a broad range of conduct models and cost functions. To overcome the curse of dimensionality, we adapt the Variat…

This https://arxiv.org/abs/2409.17800 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
Machine Learning (ML) models have gained popularity in medical imaging analysis given their expert level performance in many medical domains. To enhance the trustworthiness, acceptance, and regulatory compliance of medical imaging models and to facilitate their integration into clinical settings, we review and categorise methods for ensuring ML reliability, both during development and throughout the model's lifespan. Specifically, we provide an overview of methods assessing models' inner-workin…

Which ML techniques can emerge, and which ones should be engineered?
I've shared my perspective on this seldomly discussed topic: https://consistos.github.io

Joint Beamforming and Integer User Association using a GNN with Gumbel-Softmax Reparameterizations
Qing Lyu, Mai Vu
https://arxiv.org/abs/2506.05241 https:…

Joint Beamforming and Integer User Association using a GNN with Gumbel-Softmax Reparameterizations
Machine learning (ML) models can effectively optimize a multi-cell wireless network by designing the beamforming vectors and association decisions. Existing ML designs, however, often needs to approximate the integer association variables with a probability distribution output. We propose a novel graph neural network (GNN) structure that jointly optimize beamforming vectors and user association while guaranteeing association output as integers. The integer association constraints are satisfied …

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Mode...
Zachary C. Brown, David Carlson

@… hello! What plays as frequency source in your power backup setup with the battery of you EV as primary energy source?
I'm no electrician. From my understanding we have something like diesel generator that does not work in sync with the grid, and DC/AC converters that we want to be able to run in sync with the grid and off the grid. And as far a…

"Professor Cain uses various programming languages to explain these concepts: he starts with C (roughly from the second lecture to the 8th), then Assembly (lectures 9 to 11), C (lectures 12 to 18), Scheme (lectures 19 to 23), and Python (from 24 to 26). The last lecture (27) dives into some other functional programming languages like ML, Miranda, and even Haskell, as well as some advanced type design concepts, to round up your general programming knowledge."

Jerry Cain
The late 2000s were an interesting time for online education. The wider availability of faster and more reliable bandwidth led to an explosion of online video. This, in turn, led to the emergence of an ever-expanding number of providers of online learning services, and then to a wave of "Massive Open Online Courses" or MOOCs, many of which were offered by large universities and high schools all over the world. This month's Vidéothèque movie is a full playlist featuring one of the earliest (an…

Multilevel Bregman Proximal Gradient Descent
Yara Elshiaty, Stefania Petra
https://arxiv.org/abs/2506.03950 https://arxiv.org/pdf/250…

Multilevel Bregman Proximal Gradient Descent
We present the Multilevel Bregman Proximal Gradient Descent (ML-BPGD) method, a novel multilevel optimization framework tailored to constrained convex problems with relative Lipschitz smoothness. Our approach extends the classical multilevel optimization framework (MGOPT) to handle Bregman-based geometries and constrained domains. We provide a rigorous analysis of ML BPGD for multiple coarse levels and establish a global linear convergence rate. We demonstrate the effectiveness of ML BPGD in th…

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…

Evolving HPC services to enable ML workloads on HPE Cray EX
Stefano Schuppli, Fawzi Mohamed, Henrique Mendon\c{c}a, Nina Mujkanovic, Elia Palme, Dino Conciatore, Lukas Drescher, Miguel Gila, Pim Witlox, Joost VandeVondele, Maxime Martinasso, Thomas C. Schulthess, Torsten Hoefler
https://arxiv.org/abs/2507.01880

Evolving HPC services to enable ML workloads on HPE Cray EX
The Alps Research Infrastructure leverages GH200 technology at scale, featuring 10,752 GPUs. Accessing Alps provides a significant computational advantage for researchers in Artificial Intelligence (AI) and Machine Learning (ML). While Alps serves a broad range of scientific communities, traditional HPC services alone are not sufficient to meet the dynamic needs of the ML community. This paper presents an initial investigation into extending HPC service capabilities to better support ML workloa…

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…

The Maximum Likelihood Degree of Toric Models is Monotonic
Carlos Am\'endola, Janike Oldekop, Maximilian Wiesmann
https://arxiv.org/abs/2507.02719 http…

The Maximum Likelihood Degree of Toric Models is Monotonic
We settle a conjecture by Coons and Sullivant stating that the maximum likelihood (ML) degree of a facial submodel of a toric model is at most the ML degree of the model itself. We discuss the impact on the ML degree from observing zeros in the data. Moreover, we connect this problem to tropical likelihood degenerations, and show how the results can be applied to discrete graphical and quasi-independence models.

Enduring Disparities in the Workplace: A Pilot Study in the AI Community
Yunusa Simipa Abdulsalam, Siobhan Mackenzie Hall, Ana Quintero-Ossa, William Agnew, Carla Muntean, Sarah Tan, Ashley Heady, Savannah Thais, Jessica Schrouff
https://arxiv.org/abs/2506.04305

Enduring Disparities in the Workplace: A Pilot Study in the AI Community
In efforts toward achieving responsible artificial intelligence (AI), fostering a culture of workplace transparency, diversity, and inclusion can breed innovation, trust, and employee contentment. In AI and Machine Learning (ML), such environments correlate with higher standards of responsible development. Without transparency, disparities, microaggressions and misconduct will remain unaddressed, undermining the very structural inequities responsible AI aims to mitigate. While prior work invest…

Hi @…,
as you implemented #RFC9421 you may know - which servers in the wild use it?
#Activitypub

Emerging ML-AI Techniques for Analog and RF EDA
Zhengfeng Wu, Ziyi Chen, Nnaemeka Achebe, Vaibhav V. Rao, Pratik Shrestha, Ioannis Savidis
https://arxiv.org/abs/2506.00007

Emerging ML-AI Techniques for Analog and RF EDA
This survey explores the integration of machine learning (ML) into EDA workflows for analog and RF circuits, addressing challenges unique to analog design, which include complex constraints, nonlinear design spaces, and high computational costs. State-of-the-art learning and optimization techniques are reviewed for circuit tasks such as constraint formulation, topology generation, device modeling, sizing, placement, and routing. The survey highlights the capability of ML to enhance automation, …

This https://arxiv.org/abs/2502.14627 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

ATRI: Mitigating Multilingual Audio Text Retrieval Inconsistencies by Reducing Data Distribution Errors
Multilingual audio-text retrieval (ML-ATR) is a challenging task that aims to retrieve audio clips or multilingual texts from databases. However, existing ML-ATR schemes suffer from inconsistencies for instance similarity matching across languages. We theoretically analyze the inconsistency in terms of both multilingual modal alignment direction error and weight error, and propose the theoretical weight error upper bound for quantifying the inconsistency. Based on the analysis of the weight err…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Non-negative matrix factorization algorithms generally improve topic model fits
Peter Carbonetto, Abhishek Sarkar, Zihao Wang, Matthew Stephens

Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs
Christiana Chamon, Abhijit Sarkar, A. Lynn Abbott
https://arxiv.org/abs/2506.05135 https://…

Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs
Wearable and implantable healthcare sensors are pivotal for real-time patient monitoring but face critical challenges in power efficiency, data security, and signal noise. This paper introduces a novel platform that leverages hardware noise as a dual-purpose resource to enhance machine learning (ML) robustness and secure data via Physical Unclonable Functions (PUFs). By integrating noise-driven signal processing, PUFbased authentication, and ML-based anomaly detection, our system achieves secur…

Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges
Raj Patel, Himanshu Tripathi, Jasper Stone, Noorbakhsh Amiri Golilarz, Sudip Mittal, Shahram Rahimi, Vini Chaudhary
https://arxiv.org/abs/2506.02032

Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges
The rapid adoption of machine learning (ML) technologies has driven organizations across diverse sectors to seek efficient and reliable methods to accelerate model development-to-deployment. Machine Learning Operations (MLOps) has emerged as an integrative approach addressing these requirements by unifying relevant roles and streamlining ML workflows. As the MLOps market continues to grow, securing these pipelines has become increasingly critical. However, the unified nature of MLOps ecosystem …

Using Diffusion Models to do Data Assimilation
Daniel Hodyss, Matthias Morzfeld
https://arxiv.org/abs/2506.02249 https://arxiv.org/pd…

Using Diffusion Models to do Data Assimilation
The recent surge in machine learning (ML) methods for geophysical modeling has raised the question of how these same ML methods might be applied to data assimilation (DA). We focus on diffusion modeling (a form of generative artificial intelligence) and on systems that can perform the entire DA, rather than on ML-based tools that are used within an otherwise conventional DA system. We explain that there are (at least) three different types of diffusion-based DA systems and we show in detail tha…

camelCase
snake_case
PascalCase
kebab-case
SCREAM_CASE
jerkcase

ML-Driven Strong Lens Discoveries: Down to $\theta_E \sim 0.03''$ and $M_\mathrm{halo}< 10^{11} M_\odot$
Ethan Silver, R. Wang, Xiaosheng Huang, A. Bolton, C. Storfer, S. Banka
https://arxiv.org/abs/2507.01943

ML-Driven Strong Lens Discoveries: Down to $θ_E \sim 0.03''$ and $M_\mathrm{halo}< 10^{11} M_\odot$
We present results on extending the strong lens discovery space down to much smaller Einstein radii ($θ_E\lesssim0.03''$) and much lower halo mass ($M_\mathrm{halo}<10^{11}M_\odot$) through the combination of JWST observations and machine learning (ML) techniques. First, we forecast detectable strong lenses with JWST using CosmoDC2 as the lens catalog, and a source catalog down to 29th magnitude. By further incorporating the VELA hydrodynamical simulations of high-redshift galaxies, we simulat…

AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software
Satoshi Tanaka, Samrat Thapa, Kok Seang Tan, Amadeusz Szymko, Lobos Kenzo, Koji Minoda, Shintaro Tomie, Kotaro Uetake, Guolong Zhang, Isamu Yamashita, Takamasa Horibe
https://arxiv.org/abs/2506.00645

AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software
In recent years, machine learning technologies have played an important role in robotics, particularly in the development of autonomous robots and self-driving vehicles. As the industry matures, robotics frameworks like ROS 2 have been developed and provides a broad range of applications from research to production. In this work, we introduce AWML, a framework designed to support MLOps for robotics. AWML provides a machine learning infrastructure for autonomous driving, supporting not only the …

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…

#Plex, stop! I only have so much popcorn to eat while I watch all these new changes play out🍿
https://lemmy.ml/post/30839839

ML-based muon identification using a FNAL-NICADD scintillator chamber for the MID subsystem of ALICE 3
Jesus Eduardo Mu\~noz Mendez, Antonio Ortiz, Alom Antonio Paz Jimenez, Paola Vargas Torres, Ruben Alfaro Molina, Laura Helena Gonz\'alez Trueba, Varlen Grabski, Arturo Fernandez Tellez, Hector David Regules Medel, Mario Rodriguez Cahuantzi, Guillermo Tejeda Mu\~noz, Yael Antonio Vasquez Beltran, Juan Carlos Cabanillas Noris, Solangel Rojas Torres, Gergely Gabor Barnafoldi, Daniel …

ML-based muon identification using a FNAL-NICADD scintillator chamber for the MID subsystem of ALICE 3
The ALICE Collaboration is planning to construct a new detector (ALICE 3) aiming at exploiting the potential of the high-luminosity Large Hadron Collider (LHC). The new detector will allow ALICE to participate in LHC Run 5 scheduled from 2036 to 2041. The muon-identifier subsystem (MID) is part of the ALICE 3 reference detector layout. The MID will consist of a standard magnetic iron absorber ($\approx4$ nuclear interaction lengths) followed by muon chambers. The baseline option for the MID cha…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization
Daesung Kim, Hye Won Chung

A Gaussian process approach for rapid evaluation of skin tension
Matt Nagle, Hannah Conroy Broderick, Christelle Vedel, Michel Destrade, Michael Fop, Aisling Ni Annaidh
https://arxiv.org/abs/2506.05118

A Gaussian process approach for rapid evaluation of skin tension
Skin tension plays a pivotal role in clinical settings, it affects scarring, wound healing and skin necrosis. Despite its importance, there is no widely accepted method for assessing in vivo skin tension or its natural pre-stretch. This study aims to utilise modern machine learning (ML) methods to develop a model that uses non-invasive measurements of surface wave speed to predict clinically useful skin properties such as stress and natural pre-stretch. A large dataset consisting of simulated w…

Deep Neural Networks Hunting Ultra-Light Dark Matter
Pavel K\r{u}s, Diana L\'opez Nacir, Federico R. Urban
https://arxiv.org/abs/2506.04100 https://

Deep Neural Networks Hunting Ultra-Light Dark Matter
Ultra-light dark matter (ULDM) is a compelling candidate for cosmological dark matter. If ULDM interacts with ordinary matter, it can induce measurable, characteristic signals in pulsar-timing data because it causes the orbits of pulsars in binary systems to osculate. In this work, we investigate the potential of machine learning (ML) techniques to detect such ULDM signals. To this end, we construct three types of neural networks: an autoencoder, a binary classifier, and a multiclass classifier…

Human-Machine Collaboration-Guided Space Design: Combination of Machine Learning Models and Humanistic Design Concepts
Yuxuan Yang
https://arxiv.org/abs/2507.01776

Human-Machine Collaboration-Guided Space Design: Combination of Machine Learning Models and Humanistic Design Concepts
The integration of machine learning (ML) into spatial design holds immense potential for optimizing space utilization, enhancing functionality, and streamlining design processes. ML can automate tasks, predict performance outcomes, and tailor spaces to user preferences. However, the emotional, cultural, and aesthetic dimensions of design remain crucial for creating spaces that truly resonate with users-elements that ML alone cannot address. The key challenge lies in harmonizing data-driven effi…

Geminet: Learning the Duality-based Iterative Process for Lightweight Traffic Engineering in Changing Topologies
Ximeng Liu, Shizhen Zhao, Xinbing Wang
https://arxiv.org/abs/2506.23640

Geminet: Learning the Duality-based Iterative Process for Lightweight Traffic Engineering in Changing Topologies
Recently, researchers have explored ML-based Traffic Engineering (TE), leveraging neural networks to solve TE problems traditionally addressed by optimization. However, existing ML-based TE schemes remain impractical: they either fail to handle topology changes or suffer from poor scalability due to excessive computational and memory overhead. To overcome these limitations, we propose Geminet, a lightweight and scalable ML-based TE framework that can handle changing topologies. Geminet is built…

Comparative analysis of computational approaches for predicting Transthyretin transcription activators and human dopamine D1 receptor antagonists
Mariya L. Ivanova, Nicola Russo, Konstantin Nikolic
https://arxiv.org/abs/2506.01137

Comparative analysis of computational approaches for predicting Transthyretin transcription activators and human dopamine D1 receptor antagonists
The study expands the application of scikit-learn-based machine learning (ML) to the prediction of small biomolecule functionalities based on Carbon 13 isotope (13C) NMR spectroscopy data derived from Simplified Molecular Input Line Entry System (SMILES) notations. The methodology previously demonstrated by predicting dopamine D1 receptor antagonists was upgraded with addition of new molecular features derived from the PubChem database. The enhanced ML model obtained 75.8% Accuracy, 84.2% Preci…

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[2/2]:
- Accelerating Photonic Integrated Circuit Design: Traditional, ML and Quantum Methods
Oquendo, Nadir, Jonuzi, Patra, Sinha, Or\'us, Mugel

How do Pre-Trained Models Support Software Engineering? An Empirical Study in Hugging Face
Alexandra Gonz\'alez, Xavier Franch, David Lo, Silverio Mart\'inez-Fern\'andez
https://arxiv.org/abs/2506.03013

How do Pre-Trained Models Support Software Engineering? An Empirical Study in Hugging Face
Open-Source Pre-Trained Models (PTMs) provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailored to Software Engineering (SE) needs. To address this gap, we derive a taxonomy encompassing 147 SE tasks and apply an SE-oriented classification to PTMs in a popular open-source ML repository, Hugging Face (HF). Our repository mining study began with a systematically gathered database of PTMs from the HF API, considering their model card de…

Use multi level models with {parsnip}: http://multilevelmod.tidymodels.org/ #rstats #ML

What annoys me the most about the current state of AI
I've dabbled with ML ever since the very first stanford ML class. That was almost 15 years ago.
On a technical level, it is mind meltingly insane. Just the fact that the stuff that was released in just half a decade works *at all* the way it does is just... amazing.
There is genunely amazing tech in there. Especially in pattern recognition and processing.
But then it gets tarnished by *gestures around broadly*

[2025-07-07 Mon (UTC), no new articles found for stat.ML Machine Learning]
toXiv_bot_toot

Cross-Modal Characterization of Thin Film MoS$_2$ Using Generative Models
Isaiah A. Moses, Chen Chen, Joan M. Redwing, Wesley F. Reinhart
https://arxiv.org/abs/2505.24065

Cross-Modal Characterization of Thin Film MoS$_2$ Using Generative Models
The growth and characterization of materials using empirical optimization typically requires a significant amount of expert time, experience, and resources. Several complementary characterization methods are routinely performed to determine the quality and properties of a grown sample. Machine learning (ML) can support the conventional approaches by using historical data to guide and provide speed and efficiency to the growth and characterization of materials. Specifically, ML can provide quant…

Real-Time In-Network Machine Learning on P4-Programmable FPGA SmartNICs with Fixed-Point Arithmetic and Taylor
Mohammad Firas Sada, John J. Graham, Mahidhar Tatineni, Dmitry Mishin, Thomas A. DeFanti, Frank W\"urthwein
https://arxiv.org/abs/2507.00428

Real-Time In-Network Machine Learning on P4-Programmable FPGA SmartNICs with Fixed-Point Arithmetic and Taylor
As machine learning (ML) applications become integral to modern network operations, there is an increasing demand for network programmability that enables low-latency ML inference for tasks such as Quality of Service (QoS) prediction and anomaly detection in cybersecurity. ML models provide adaptability through dynamic weight adjustments, making Programming Protocol-independent Packet Processors (P4)-programmable FPGA SmartNICs an ideal platform for investigating In-Network Machine Learning (IN…

This https://arxiv.org/abs/2505.24200 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC
Multilingual speech processing with self-supervised or supervised pre-trained Speech Foundation Models (SFM) has achieved strong performance on tasks like Language Identification (LID) and Automatic Speech Recognition (ASR). However, these models struggle with limited resources during fine-tuning. This paper enhances multilingual LID and ASR on ML-SUPERB 2.0 by exploring multiple strategies for adapting SFMs, including frozen upstream training, partial fine-tuning, and low-rank adaptation. Furt…

Zohran Mamdani received 545,334 votes in RCV tally. That's 135% of Eric Adams' 2021 primary vote.
Mamdani's proven primary voters would make a 48% share of those who turned out in the 2021 general election.
https://enr.boenyc.gov/rcv/026916_1.html

At Berlin Buzzwords 2025, join Dhrubo Saha to discover how OpenSearch pipelines are integrating ML inference processors for powerful multi-modal search. Learn to search directly within images, audio, and text – locally on your own hardware!
Learn more: https://

[2025-06-06 Fri (UTC), 7 new articles found for stat.ML Machine Learning]
#toXiv_bot_toot

Plasmonic detection of Rashba spin-orbit coupling in monolayer transition-metal dichalcogenides
Y. Li, Z. H. Tao, Y. M. Xiao, W. Xu, Q. N. Li, F. M. Peeters, D. Neilson, M. V. Milosevic
https://arxiv.org/abs/2507.00567

Plasmonic detection of Rashba spin-orbit coupling in monolayer transition-metal dichalcogenides
Rashba spin-orbit coupling (RSOC) induces strong momentum-dependent spin splitting and plays a crucial role in fields like spintronics and topological photonics. We here theoretically investigate the collective excitations in monolayer transition metal dichalcogenides (ML-TMDs) hosting RSOC, and conceive an approach to precisely quantify the strength of RSOC using plasmons. We determine the electron energy loss function (EELF) and plasmon dispersions for n-type ML-TMD from the dynamic dielectri…

This https://arxiv.org/abs/2505.23337 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Matryoshka Model Learning for Improved Elastic Student Models
Industry-grade ML models are carefully designed to meet rapidly evolving serving constraints, which requires significant resources for model development. In this paper, we propose MatTA, a framework for training multiple accurate Student models using a novel Teacher-TA-Student recipe. TA models are larger versions of the Student models with higher capacity, and thus allow Student models to better relate to the Teacher model and also bring in more domain-specific expertise. Furthermore, multiple…

@… I very much agree with many of your points, and I’m glad that I no longer work in CL—which had already become NLP a long time ago, and is now by many considered “a subfield of ML.”
I think the problem started when computational linguistics and linguistics split into separate disciplines: one less and less interested in language, the other less…

@… I very much agree with many of your points, and I’m glad that I no longer work in CL—which had already become NLP a long time ago, and is now by many considered “a subfield of ML.”
I think the problem started when computational linguistics and linguistics split into separate disciplines: one less and less interested in language, the other less…

Thoughts on running local, privacy aware LLMs and other AI/ML via GPT4All, PrivateGPT, OLMo 2 or Ollama?
I have an M3 Macbook Air and limited capacity for faff right now

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad, Aditya Sant, David J. Love, Christopher G. Brinton
https://arxiv.org/abs/2506.00374

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML systems for wireless communications is the availability of realistic wireless channel datasets, which are extremely resource-intensive to produce. To this end, the generation of realistic wireless channels plays a key role in the subsequent design of effective ML algorithms for wireless communication systems. Generativ…

Stabilizing PDE--ML Coupled System
Saad Qadeer, Panos Stinis, Hui Wan
https://arxiv.org/abs/2506.19274 https://arxiv.org/pdf/2506.192…

Stabilizing PDE--ML Coupled System
A long-standing obstacle in the use of machine-learnt surrogates with larger PDE systems is the onset of instabilities when solved numerically. Efforts towards ameliorating these have mostly concentrated on improving the accuracy of the surrogates or imbuing them with additional structure, and have garnered limited success. In this article, we study a prototype problem and draw insights that can help with more complex systems. In particular, we focus on a viscous Burgers'-ML system and, after i…

Breaking Out from the TESSERACT: Reassessing ML-based Malware Detection under Spatio-Temporal Drift
Theo Chow, Mario D'Onghia, Lorenz Linhardt, Zeliang Kan, Daniel Arp, Lorenzo Cavallaro, Fabio Pierazzi
https://arxiv.org/abs/2506.23814

Breaking Out from the TESSERACT: Reassessing ML-based Malware Detection under Spatio-Temporal Drift
Several recent works focused on the best practices for applying machine learning to cybersecurity. In the context of malware, TESSERACT highlighted the impact of concept drift on detection performance and suggested temporal and spatial constraints to be enforced to ensure realistic time-aware evaluations, which have been adopted by the community. In this paper, we demonstrate striking discrepancies in the performance of learning-based malware detection across the same time frame when evaluated …

Нативщик? Объясни почему раньше js исполнялся только в браузере, а теперь половина софта на моем компьютере и телефоне это браузер притворяющийся приложением? А на каждый клик мне в винде вылетает модальное окно "хотите разрешить изменять файлы на диске"? И эти модалки с правами гребанные везде, причем пришли с телефонов сначала.
Технологии вроде бы шагнули вперед, упростились, а опыт для человека лучше не стал. Получается не шагнули вперед и не упростились? Почему разработчи…

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Tinghuan Li, Shuheng Chen, Junyi Fan, Elham Pishgar, Kamiar Alaei, Greg Placencia, Maryam Pishgar
https://arxiv.org/abs/2506.03209

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalization, elevated healthcare costs, and increased mortality. Accurate early risk stratification is essential to enable timely intervention and improve clinical outcomes. We constructed a combined cohort of 19,085 elderly SICU admissions from the MIMIC-III and MIMIC-IV databases and developed an interpretable machine learning (ML) framework to predict in…

Define-ML: An Approach to Ideate Machine Learning-Enabled Systems
Silvio Alonso, Antonio Pedro Santos Alves, Lucas Romao, H\'elio Lopes, Marcos Kalinowski
https://arxiv.org/abs/2506.20621

Define-ML: An Approach to Ideate Machine Learning-Enabled Systems
[Context] The increasing adoption of machine learning (ML) in software systems demands specialized ideation approaches that address ML-specific challenges, including data dependencies, technical feasibility, and alignment between business objectives and probabilistic system behavior. Traditional ideation methods like Lean Inception lack structured support for these ML considerations, which can result in misaligned product visions and unrealistic expectations. [Goal] This paper presents Define-M…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a ...
Pedro Pessoa, Juan Andres Martinez, Vincent Vandenbroucke, Frank Delvigne, Steve Press\'e

Нативщик? Объясни почему раньше js исполнялся только в браузере, а теперь половина софта на моем компьютере и телефоне это браузер притворяющийся приложением? А на каждый клик мне в винде вылетает модальное окно "хотите разрешить изменять файлы на диске"? И эти модалки с правами гребанные везде, причем пришли с телефонов сначала.
Технологии вроде бы шагнули вперед, упростились, а опыт для человека лучше не стал. Получается не шагнули вперед и не упростились? Почему разработчи…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- An Unconditional Representation of the Conditional Score in Infinite-Dimensional Linear Inverse P...
Fabian Schneider, Duc-Lam Duong, Matti Lassas, Maarten V. de Hoop, Tapio Helin

This https://arxiv.org/abs/2505.04608 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales
Responsibly deploying artificial intelligence (AI) / machine learning (ML) systems in high-stakes settings arguably requires not only proof of system reliability, but also continual, post-deployment monitoring to quickly detect and address any unsafe behavior. Methods for nonparametric sequential testing -- especially conformal test martingales (CTMs) and anytime-valid inference -- offer promising tools for this monitoring task. However, existing approaches are restricted to monitoring limited …

KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs
Xin Fan Guo, Albert Merono Penuela, Sergio Maffeis, Fabio Pierazzi
https://arxiv.org/abs/2506.19802

KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs
Despite extensive research on Machine Learning-based Network Intrusion Detection Systems (ML-NIDS), their capability to detect diverse attack variants remains uncertain. Prior studies have largely relied on homogeneous datasets, which artificially inflate performance scores and offer a false sense of security. Designing systems that can effectively detect a wide range of attack variants remains a significant challenge. The progress of ML-NIDS continues to depend heavily on human expertise, whic…

SuperSONIC: Cloud-Native Infrastructure for ML Inferencing
Dmitry Kondratyev, Benedikt Riedel, Yuan-Tang Chou, Miles Cochran-Branson, Noah Paladino, David Schultz, Mia Liu, Javier Duarte, Philip Harris, Shih-Chieh Hsu
https://arxiv.org/abs/2506.20657

SuperSONIC: Cloud-Native Infrastructure for ML Inferencing
The increasing computational demand from growing data rates and complex machine learning (ML) algorithms in large-scale scientific experiments has driven the adoption of the Services for Optimized Network Inference on Coprocessors (SONIC) approach. SONIC accelerates ML inference by offloading it to local or remote coprocessors to optimize resource utilization. Leveraging its portability to different types of coprocessors, SONIC enhances data processing and model deployment efficiency for cuttin…

This https://arxiv.org/abs/2505.05532 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Design and FPGA Implementation of WOMBAT: A Deep Neural Network Level-1 Trigger System for Jet Substructure Identification and Boosted $H\rightarrow b\bar{b}$ Tagging at the CMS Experiment
This thesis investigates the physics performance, trigger efficiency, and Field Programmable Gate Array (FPGA) implementation of machine learning (ML)-based algorithms for Lorentz-boosted $H\rightarrow b\bar{b}$ tagging within the CMS Level-1 Trigger (L1T) under Phase-1 conditions. The proposed algorithm, WOMBAT (Wide Object ML Boosted Algorithm Trigger), comprises a high-performance Master Model (W-MM) and a quantized, FPGA-synthesizable Apprentice Model (W-AM), benchmarked against the standar…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[3/3]:
- Connecting phases of matter to the flatness of the loss landscape in analog variational quantum a...
Kasidit Srimahajariyapong, Supanut Thanasilp, Thiparat Chotibut

A High-Performance Multilevel Framework for Quantum Layout Synthesis
Shuohao Ping, Naren Sathishkumar, Wan-Hsuan Lin, Hanyu Wang, Jason Cong
https://arxiv.org/abs/2505.24169

A High-Performance Multilevel Framework for Quantum Layout Synthesis
Quantum Layout Synthesis (QLS) is a critical compilation stage that adapts quantum circuits to hardware constraints with an objective of minimizing the SWAP overhead. While heuristic tools demonstrate good efficiency, they often produce suboptimal solutions, and exact methods suffer from limited scalability. In this work, we propose ML-SABRE, a high-performance multilevel framework for QLS that improves both solution quality and compilation time through a hierarchical optimization approach. We …

Что нужно знать о порядочности Дурова:
Забанить канал координации кандидатов в России во время выборов — так точно, тов. майор! :blobcatsalute:
Забанить канал координации кандидатов в Румынии во время выборов — мы не позволим разрушать демократию. Твердо и четко! :blobancap:
https://t.me/durov_russia/34

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/3]:
- Sparsing Law: Towards Large Language Models with Greater Activation Sparsity
Luo, Song, Han, Chen, Xiao, Meng, Deng, Wei, Liu, Sun

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance
Joner Assolin, Gabriel Canto, Diego Kreutz, Eduardo Feitosa, Hendrio Bragan\c{c}a, Angelo Nogueira, Vanderson Rocha
https://arxiv.org/abs/2506.23314

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance
Malware detection in Android systems requires both cybersecurity expertise and machine learning (ML) techniques. Automated Machine Learning (AutoML) has emerged as an approach to simplify ML development by reducing the need for specialized knowledge. However, current AutoML solutions typically operate as black-box systems with limited transparency, interpretability, and experiment traceability. To address these limitations, we present MH-AutoML, a domain-specific framework for Android malware d…

Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
Dani Korpi, Rachel Wang, Jerry Wang, Abdelrahman Ibrahim, Carl Nuzman, Runxin Wang, Kursat Rasim Mestav, Dustin Zhang, Iraj Saniee, Shawn Winston, Gordana Pavlovic, Wei Ding, William J. Hillery, Chenxi Hao, Ram Thirunagari, Jung Chang, Jeehyun Kim, Bartek Kozicki, Dragan Samardzija, Taesang Yoo, Andreas Maeder, Tingfang Ji, Harish Viswanathan

Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
Neural network-based compression and decompression of channel state feedback has been one of the most widely studied applications of machine learning (ML) in wireless networks. Various simulation-based studies have shown that ML-based feedback compression can result in reduced overhead and more accurate channel information. However, to the best of our knowledge, there are no real-life proofs of concepts demonstrating the benefits of ML-based channel feedback compression in a practical setting, …

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/3]:
- Convergence analysis of online algorithms for vector-valued kernel regression
Michael Griebel, Peter Oswald

eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems
Ruilin Xu, Zongxuan Xie, Pengfei Chen
https://arxiv.org/abs/2506.02007

eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems
We present eACGM, a full-stack AI/ML system monitoring framework based on eBPF. eACGM collects real-time performance data from key hardware components, including the GPU and network communication layer, as well as from key software stacks such as CUDA, Python, and PyTorch, all without requiring any code instrumentation or modifications. Additionally, it leverages libnvml to gather process-level GPU resource usage information. By applying a Gaussian Mixture Model (GMM) to the collected multidime…

Что нужно знать о порядочности Дурова:
Забанить канал координации кандидатов в России во время выборов — так точно, тов. майор! :blobcatsalute:
Забанить канал координации кандидатов в Румынии во время выборов — мы не позволим разрушать демократию. Твердо и четко! :blobancap:
https://t.me/durov_russia/34

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…

Agile Management for Machine Learning: A Systematic Mapping Study
Lucas Romao, Hugo Villamizar, Romeu Oliveira, Silvio Alonso, Marcos Kalinowski
https://arxiv.org/abs/2506.20759

Agile Management for Machine Learning: A Systematic Mapping Study
[Context] Machine learning (ML)-enabled systems are present in our society, driving significant digital transformations. The dynamic nature of ML development, characterized by experimental cycles and rapid changes in data, poses challenges to traditional project management. Agile methods, with their flexibility and incremental delivery, seem well-suited to address this dynamism. However, it is unclear how to effectively apply these methods in the context of ML-enabled systems, where challenges …

Alleviating Attack Data Scarcity: SCANIA's Experience Towards Enhancing In-Vehicle Cyber Security Measures
Frida Sundfeldt, Bianca Widstam, Mahshid Helali Moghadam, Kuo-Yun Liang, Anders Vesterberg
https://arxiv.org/abs/2507.02607

Alleviating Attack Data Scarcity: SCANIA's Experience Towards Enhancing In-Vehicle Cyber Security Measures
The digital evolution of connected vehicles and the subsequent security risks emphasize the critical need for implementing in-vehicle cyber security measures such as intrusion detection and response systems. The continuous advancement of attack scenarios further highlights the need for adaptive detection mechanisms that can detect evolving, unknown, and complex threats. The effective use of ML-driven techniques can help address this challenge. However, constraints on implementing diverse attack…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Explicit neural network classifiers for non-separable data
Patr\'icia Mu\~noz Ewald

А поясните чем доктору решетки эти так сентиментальны? То, что месяц работы в пизду это я понимаю. Но что-то же его заставило в эти решетки эту кучу времени и так сказать любви вложить, он же вроде мог останавливаться ещё на первом покрасе...
Не знаю, я может злорадный и бессердечный что не могу понять чем эти решетки ржавые так важны?.. Может я просто не достаточно следил и пропустил важный момент какой-то?

This https://arxiv.org/abs/2205.09337 has been replaced.
link: https://scholar.google.com/scholar?q=a

Deep Learning in Business Analytics: A Clash of Expectations and Reality
Our fast-paced digital economy shaped by global competition requires increased data-driven decision-making based on artificial intelligence (AI) and machine learning (ML). The benefits of deep learning (DL) are manifold, but it comes with limitations that have, so far, interfered with widespread industry adoption. This paper explains why DL, despite its popularity, has difficulties speeding up its adoption within business analytics. It is shown that the adoption of deep learning is not only aff…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings
C. Shi, S. Zhang, W. Lu, R. Song

This https://arxiv.org/abs/2506.00377 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT
The widespread adoption of the Internet of Things (IoT) has raised a new challenge for developers since it is prone to known and unknown cyberattacks due to its heterogeneity, flexibility, and close connectivity. To defend against such security breaches, researchers have focused on building sophisticated intrusion detection systems (IDSs) using machine learning (ML) techniques. Although these algorithms notably improve detection performance, they require excessive computing power and resources,…

Маленькая инди компания решила сделать кнопку сжимания меню в виде grip indicator'а
Авось когда-нибудь действительно его можно будет перетаскивать, но сейчас только кликать... Прогрессивное улучшение!
#yandex #UXHell

Маленькая инди компания решила сделать кнопку сжимания меню в виде grip indicator'а
Авось когда-нибудь действительно его можно будет перетаскивать, но сейчас только кликать... Прогрессивное улучшение!
#yandex #UXHell

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- High-dimensional Contextual Bandit Problem without Sparsity
Junpei Komiyama, Masaaki Imaizumi

Отличная статейка про ИИ:
- и про луддитов, воевавших с подпиской на одежду
- и про двигатели Апполона, которые мы знаем как делаются, но сделать больше никогда не сможем
- и про сеошников, которые пишут для кого угодно, но не для людей
- и про костыльность новейших технологий и этичность антропика
- и про выедание интернета гигантами с добросовестным использованием
- и про человеческие жертвы богу нашему Super Intelligence, который неизбежно всех спасет через …

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Proofs as Explanations: Short Certificates for Reliable Predictions
Avrim Blum, Steve Hanneke, Chirag Pabbaraju, Donya Saless

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Efficient uniform approximation using Random Vector Functional Link networks
Palina Salanevich, Olov Schavemaker

Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction
Johan Hallberg Szabadv\'ary, Tuwe L\"ofstr\"om, Ulf Johansson, Cecilia S\"onstr\"od, Ernst Ahlberg, Lars Carlsson
https://arxiv.org/abs/2506.21802

Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction
Machine learning (ML) models always make a prediction, even when they are likely to be wrong. This causes problems in practical applications, as we do not know if we should trust a prediction. ML with reject option addresses this issue by abstaining from making a prediction if it is likely to be incorrect. In this work, we formalise the approach to ML with reject option in binary classification, deriving theoretical guarantees on the resulting error rate. This is achieved through conformal pred…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Constructive Universal Approximation and Finite Sample Memorization by Narrow Deep ReLU Networks
Mart\'in Hern\'andez, Enrique Zuazua

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset...
Azmine Toushik Wasi, MD Shafikul Islam, Adipto Raihan Akib, Mahathir Mohammad Bappy

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Uncertainty-aware Efficient Subgraph Isomorphism using Graph Topology
Arpan Kusari, Wenbo Sun

[2025-07-04 Fri (UTC), 5 new articles found for stat.ML Machine Learning]
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Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension
Yijun Dong, Yicheng Li, Yunai Li, Jason D. Lee, Qi Lei

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Expe...
Qianglin Wen, Chengchun Shi, Ying Yang, Niansheng Tang, Hongtu Zhu

[2025-07-03 Thu (UTC), 4 new articles found for stat.ML Machine Learning]
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Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Local minima of the empirical risk in high dimension: General theorems and convex examples
Kiana Asgari, Andrea Montanari, Basil Saeed

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
Semiparametric Bayesian Difference-in-Differences
https://

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
How Sparse Can We Prune A Deep Network: A Fundamental Limit Viewpoint

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
Generalized Random Forests using Fixed-Point Trees
https://…

[2025-07-02 Wed (UTC), 5 new articles found for stat.ML Machine Learning]
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