Tootfinder

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017
Zhaoyang Xu, Yunbo Liu
https://arxiv.org/abs/2506.19877 https…

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017
Identifying suitable machine learning paradigms for intrusion detection remains critical for building effective and generalizable security solutions. In this study, we present a controlled comparison of four representative models - Multi-Layer Perceptron (MLP), 1D Convolutional Neural Network (CNN), One-Class Support Vector Machine (OCSVM) and Local Outlier Factor (LOF) - on the CICIDS2017 dataset under two scenarios: detecting known attack types and generalizing to previously unseen threats. O…

Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models
Dominik Stempie\'n, Robert \'Slepaczuk
https://arxiv.org/abs/2505.19617

Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models
This research systematically develops and evaluates various hybrid modeling approaches by combining traditional econometric models (ARIMA and ARFIMA models) with machine learning and deep learning techniques (SVM, XGBoost, and LSTM models) to forecast financial time series. The empirical analysis is based on two distinct financial assets: the S&P 500 index and Bitcoin. By incorporating over two decades of daily data for the S&P 500 and almost ten years of Bitcoin data, the study provides a comp…

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 …

From Tiny Machine Learning to Tiny Deep Learning: A Survey
Shriyank Somvanshi, Md Monzurul Islam, Gaurab Chhetri, Rohit Chakraborty, Mahmuda Sultana Mimi, Swagat Ahmed Shuvo, Kazi Sifatul Islam, Syed Aaqib Javed, Sharif Ahmed Rafat, Anandi Dutta, Subasish Das
https://arxiv.org/abs/2506.18927…

From Tiny Machine Learning to Tiny Deep Learning: A Survey
The rapid growth of edge devices has driven the demand for deploying artificial intelligence (AI) at the edge, giving rise to Tiny Machine Learning (TinyML) and its evolving counterpart, Tiny Deep Learning (TinyDL). While TinyML initially focused on enabling simple inference tasks on microcontrollers, the emergence of TinyDL marks a paradigm shift toward deploying deep learning models on severely resource-constrained hardware. This survey presents a comprehensive overview of the transition from…

Uncertainty-Aware Machine-Learning Framework for Predicting Dislocation Plasticity and Stress-Strain Response in FCC Alloys
Jing Luo, Yejun Gu, Yanfei Wang, Xiaolong Ma, Jaafar. A El-Awady
https://arxiv.org/abs/2506.20839

Uncertainty-Aware Machine-Learning Framework for Predicting Dislocation Plasticity and Stress-Strain Response in FCC Alloys
Machine learning has significantly advanced the understanding and application of structural materials, with an increasing emphasis on integrating existing data and quantifying uncertainties in predictive modeling. This study presents a comprehensive methodology utilizing a mixed density network (MDN) model, trained on extensive experimental data from literature. This approach uniquely predicts the distribution of dislocation density, inferred as a latent variable, and the resulting stress distr…

Predicting wide binaries and deviations from standard gravity using machine learning algorithms
Amoy Ashesh, Harsimran Kaur, Sandeep Aashish
https://arxiv.org/abs/2506.19942

Predicting wide binaries and deviations from standard gravity using machine learning algorithms
We present a machine learning (ML) framework for the detection of wide binary star systems and the identification of deviations from standard gravity using Gaia DR3 data. By training supervised ML models on established wide binary catalogues, we efficiently classify wide binaries and employ clustering and nearest neighbour search to pair candidate systems. We recast the problem of identifying systematic deviations from standard gravity as a supervised anomaly detection. Our approach incorporate…

Advancing global sea ice prediction capabilities using a fully-coupled climate model with integrated machine learning
William Gregory, Mitchell Bushuk, Yong-Fei Zhang, Alistair Adcroft, Laure Zanna, Colleen McHugh, Liwei Jia
https://arxiv.org/abs/2505.18328

Advancing global sea ice prediction capabilities using a fully-coupled climate model with integrated machine learning
We showcase a hybrid modeling framework which embeds machine learning (ML) inference into the GFDL SPEAR climate model, for online sea ice bias correction during a set of global fully-coupled 1-year retrospective forecasts. We compare two hybrid versions of SPEAR to understand the importance of exposing ML models to coupled ice-atmosphere-ocean feedbacks before implementation into fully-coupled simulations: Hybrid_CPL (with feedbacks) and Hybrid_IO (without feedbacks). Relative to SPEAR, Hybrid…

LARP: Learner-Agnostic Robust Data Prefiltering
Kristian Minchev, Dimitar Iliev Dimitrov, Nikola Konstantinov
https://arxiv.org/abs/2506.20573 https://

LARP: Learner-Agnostic Robust Data Prefiltering
The widespread availability of large public datasets is a key factor behind the recent successes of statistical inference and machine learning methods. However, these datasets often contain some low-quality or contaminated data, to which many learning procedures are sensitive. Therefore, the question of whether and how public datasets should be prefiltered to facilitate accurate downstream learning arises. On a technical level this requires the construction of principled data prefiltering metho…

Physics-Informed Machine Learning Approach to Modeling Line Emission from Helium-Containing Plasmas
Shin Kajita
https://arxiv.org/abs/2506.20117 https://…

Physics-Informed Machine Learning Approach to Modeling Line Emission from Helium-Containing Plasmas
The helium I line intensity ratio (LIR) method is used to measure the electron density ($n_e$) and temperature ($T_e$) of fusion-relevant plasmas. Although the collisional-radiative model (CRM) has been used to predict $n_e$ and $T_e$, recent studies have shown that machine learning approaches can provide better measurements if a sufficient dataset for training is available. This study investigates a hybrid neural network approach that combines CRM- and experiment-based models. Although the CRM…

Surrogate normal-forms for the numerical bifurcation and stability analysis of navier-stokes flows via machine learning
Alessandro Della Pia, Dimitrios G. Patsatzis, Gianluigi Rozza, Lucia Russo, Constantinos Siettos
https://arxiv.org/abs/2506.21275

Surrogate normal-forms for the numerical bifurcation and stability analysis of navier-stokes flows via machine learning
Inspired by the Equation-Free multiscale modeling approach, we demonstrate how the embed-learn-lift framework enables the construction of surrogate normal-forms, namely minimal-dimensional reduced-order models (ROMs), from high-fidelity Navier-Stokes simulations. These surrogate models are then used for efficient and accurate bifurcation and stability analysis. The framework proceeds in four steps. First, manifold learning reveals the intrinsic latent dimension of the high-dimensional spatio-te…

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…

This https://arxiv.org/abs/2502.00874 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDL_…

Paper Copilot Position: The Artificial Intelligence and Machine Learning Community Should Adopt a More Transparent and Regulated Peer Review Process
The rapid growth of submissions to top-tier Artificial Intelligence (AI) and Machine Learning (ML) conferences has prompted many venues to transition from closed to open review platforms. Some have fully embraced open peer reviews, allowing public visibility throughout the process, while others adopt hybrid approaches, such as releasing reviews only after final decisions or keeping reviews private despite using open peer review systems. In this work, we analyze the strengths and limitations of …

Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization
Yuheng Chen, Alexander Montes McNeil, Taehyuk Park, Blake A. Wilson, Vaishnavi Iyer, Michael Bezick, Jae-Ik Choi, Rohan Ojha, Pravin Mahendran, Daksh Kumar Singh, Geetika Chitturi, Peigang Chen, Trang Do, Alexander V. Kildishev, Vladimir M. Shalaev, Michael Moebius, Wenshan Cai, Yongmin Liu, Alexandra Boltasseva

Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization
Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing. PDD is an iterative, five-step process that consists of: i) deriving device behavior from design parameters, ii) simulating device performance, iii) finding the optimal candidate designs from simulations, iv) fabricating the optima…

Investigating the 2024 swarm like activity offshore Kefalonia Island aided by Machine Learning algorithms
V. Anagnostou, E. Papadimitriou, V. Karakostas, T. Back
https://arxiv.org/abs/2505.17221

Investigating the 2024 swarm like activity offshore Kefalonia Island aided by Machine Learning algorithms
In March 2024, a swarm like seismic activity occurred north of Kefalonia Island, in the central Ionian Islands area. Following a machine-learning aided workflow, we compiled an enhanced seismic catalog of 2495 low to moderate magnitude earthquakes throughout a 2 month period. Spatiotemporal analysis reveals a narrow epicentral distribution of nearly E-W alignment, approximately 5km long, much longer than the length anticipated by common scaling laws for the aftershock area extension of the stro…

This https://arxiv.org/abs/2505.09551 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Fast Learning in Quantitative Finance with Extreme Learning Machine
This paper demonstrates that a broad class of problems in quantitative finance, including those previously addressed using deep neural networks, can be efficiently solved using single-layer neural networks without iterative gradient-based training, namely extreme learning machine (ELM). ELM utilizes a single-layer network with randomly initialized hidden nodes and analytically computed output weights obtained via convex optimization, enabling rapid training and inference. Both supervised and un…

Ah fuck, people are using LLMs for kernel code. They really are going to fuck over everything, aren't they?
https://lwn.net/SubscriberLink/1026558/9f3079fb392c4a9a/

Modern search demands scalable personalisation. Join Piotr Kobziakowski
at this year's Berlin Buzzwords to discover how Vespa's multi-stage ranking and tensor framework can be used for hybrid queries, multimodal retrieval, and real-time machine learning. Learn how to deploy low-latency, high-relevance search systems at petabyte scale.
Learn more:

Vespa.ai’s Personalized Search: Advanced Ranking & Tensor framework
Today’s applications require search engines to unify text, vectors, and business logic with millisecond latency at petabyte scale. It’s not easy to balance speed, relevance, and personalization for a large user population and a billion scale item base. Vespa.ai, the open-source engine powering Yahoo, Perplexity, Qwant, Vinted, Spotify addresses this through multi-stage ranking with close to data tensor operations and easy to understand custom functions. Vespa’s phased architecture enables…

B0 -> K*0 tau tau- Decay: Using Machine Learning to Separate Signal from Background
Ziyao Xiong, Qixing Deng, Yidan Sun, Junhua Yang
https://arxiv.org/abs/2506.19501

B0 -> K*0 tau+ tau- Decay: Using Machine Learning to Separate Signal from Background
This study investigates the rare decay B0 -> K*0 tau+ tau-, which is sensitive to potential violations of lepton flavor universality predicted by the Standard Model. A Monte Carlo simulated dataset containing both signal and the dominant background process B0 -> K*0 D+ D- was used to train and evaluate machine learning classifiers. After feature selection and parameter tuning, two supervised models -- Boosted Decision Trees (BDTs) and Fully Connected Neural Networks (FCNNs) -- were trained. Fea…

Automatic Depression Assessment using Machine Learning: A Comprehensive Survey
Siyang Song, Yupeng Huo, Shiqing Tang, Jiaee Cheong, Rui Gao, Michel Valstar, Hatice Gunes
https://arxiv.org/abs/2506.18915

Automatic Depression Assessment using Machine Learning: A Comprehensive Survey
Depression is a common mental illness across current human society. Traditional depression assessment relying on inventories and interviews with psychologists frequently suffer from subjective diagnosis results, slow and expensive diagnosis process as well as lack of human resources. Since there is a solid evidence that depression is reflected by various human internal brain activities and external expressive behaviours, early traditional machine learning (ML) and advanced deep learning (DL) mo…

Recommendation systems in e-commerce applications with machine learning methods
Aneta Poniszewska-Maranda, Magdalena Pakula, Bozena Borowska
https://arxiv.org/abs/2506.17287

Recommendation systems in e-commerce applications with machine learning methods
E-commerce platforms are increasingly reliant on recommendation systems to enhance user experience, retain customers, and, in most cases, drive sales. The integration of machine learning methods into these systems has significantly improved their efficiency, personalization, and scalability. This paper aims to highlight the current trends in e-commerce recommendation systems, identify challenges, and evaluate the effectiveness of various machine learning methods used, including collaborative fi…

Extreme Learning Machines for Exoplanet Simulations: A Faster, Lightweight Alternative to Deep Learning
Tara P. A. Tahseen, Lu\'is F. Sim\~oes, Kai Hou Yip, Nikolaos Nikolaou, Jo\~ao M. Mendon\c{c}a, Ingo P. Waldmann
https://arxiv.org/abs/2506.19679

Extreme Learning Machines for Exoplanet Simulations: A Faster, Lightweight Alternative to Deep Learning
Increasing resolution and coverage of astrophysical and climate data necessitates increasingly sophisticated models, often pushing the limits of computational feasibility. While emulation methods can reduce calculation costs, the neural architectures typically used--optimised via gradient descent--are themselves computationally expensive to train, particularly in terms of data generation requirements. This paper investigates the utility of the Extreme Learning Machine (ELM) as a lightweight, no…

Quantum Advantage in Learning Quantum Dynamics via Fourier coefficient extraction
Alice Barthe, Mahtab Yaghubi Rad, Michele Grossi, Vedran Dunjko
https://arxiv.org/abs/2506.17089 …

Quantum Advantage in Learning Quantum Dynamics via Fourier coefficient extraction
One of the key challenges in quantum machine learning is finding relevant machine learning tasks with a provable quantum advantage. A natural candidate for this is learning unknown Hamiltonian dynamics. Here, we tackle the supervised learning version of this problem, where we are given random examples of the inputs to the dynamics as classical data, paired with the expectation values of some observable after the time evolution, as corresponding output labels. The task is to replicate the corres…

Using Machine Learning in Analyzing Air Quality Discrepancies of Environmental Impact
Shuangbao Paul Wang, Lucas Yang, Rahouane Chouchane, Jin Guo, Michael Bailey
https://arxiv.org/abs/2506.17319

Using Machine Learning in Analyzing Air Quality Discrepancies of Environmental Impact
In this study, we apply machine learning and software engineering in analyzing air pollution levels in City of Baltimore. The data model was fed with three primary data sources: 1) a biased method of estimating insurance risk used by homeowners loan corporation, 2) demographics of Baltimore residents, and 3) census data estimate of NO2 and PM2.5 concentrations. The dataset covers 650,643 Baltimore residents in 44.7 million residents in 202 major cities in US. The results show that air pollution…

This https://arxiv.org/abs/2406.11369 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCG_…

Approximation Algorithms for Smallest Intersecting Balls
We study a general smallest intersecting ball problem and its soft-margin variant in high-dimensional Euclidean spaces for input objects that are compact and convex. These two problems link and unify a series of fundamental problems in computational geometry and machine learning, including smallest enclosing ball, polytope distance, intersection radius, $\ell_1$-loss support vector machine, $\ell_1$-loss support vector data description, and so on. Leveraging our novel framework for solving zero…

CT Radiomics-Based Explainable Machine Learning Model for Accurate Differentiation of Malignant and Benign Endometrial Tumors: A Two-Center Study
Tingrui Zhang, Honglin Wu, Zekun Jiang, Yingying Wang, Rui Ye, Huiming Ni, Chang Liu, Jin Cao, Xuan Sun, Rong Shao, Xiaorong Wei, Yingchun Sun
https://arxiv.org/abs/2506.18106

CT Radiomics-Based Explainable Machine Learning Model for Accurate Differentiation of Malignant and Benign Endometrial Tumors: A Two-Center Study
Aimed to develop and validate a CT radiomics-based explainable machine learning model for diagnosing malignancy and benignity specifically in endometrial cancer (EC) patients. A total of 83 EC patients from two centers, including 46 with malignant and 37 with benign conditions, were included, with data split into a training set (n=59) and a testing set (n=24). The regions of interest (ROIs) were manually segmented from pre-surgical CT scans, and 1132 radiomic features were extracted from the pr…

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…

Why Robots Are Bad at Detecting Their Mistakes: Limitations of Miscommunication Detection in Human-Robot Dialogue
Ruben Janssens, Jens De Bock, Sofie Labat, Eva Verhelst, Veronique Hoste, Tony Belpaeme
https://arxiv.org/abs/2506.20268

Why Robots Are Bad at Detecting Their Mistakes: Limitations of Miscommunication Detection in Human-Robot Dialogue
Detecting miscommunication in human-robot interaction is a critical function for maintaining user engagement and trust. While humans effortlessly detect communication errors in conversations through both verbal and non-verbal cues, robots face significant challenges in interpreting non-verbal feedback, despite advances in computer vision for recognizing affective expressions. This research evaluates the effectiveness of machine learning models in detecting miscommunications in robot dialogue. U…

Learning from the Storm: A Multivariate Machine Learning Approach to Predicting Hurricane-Induced Economic Losses
Bolin Shen, Eren Erman Ozguven, Yue Zhao, Guang Wang, Yiqun Xie, Yushun Dong
https://arxiv.org/abs/2506.17964

Learning from the Storm: A Multivariate Machine Learning Approach to Predicting Hurricane-Induced Economic Losses
Florida is particularly vulnerable to hurricanes, which frequently cause substantial economic losses. While prior studies have explored specific contributors to hurricane-induced damage, few have developed a unified framework capable of integrating a broader range of influencing factors to comprehensively assess the sources of economic loss. In this study, we propose a comprehensive modeling framework that categorizes contributing factors into three key components: (1) hurricane characteristics…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- mSTEB: Massively Multilingual Evaluation of LLMs on Speech and Text Tasks
Beyene, Verma, Ma, Alabi, Schmidt, Nakatumba-Nabende, Adelani

Machine Learning-Based Near-Field Localization in Mixed LoS/NLoS Scenarios
Parisa Ramezani, Seyed Jalaleddin Mousavirad, Mattias O'Nils, Emil Bj\"ornson
https://arxiv.org/abs/2506.17810

Machine Learning-Based Near-Field Localization in Mixed LoS/NLoS Scenarios
The conventional MUltiple SIgnal Classification (MUSIC) algorithm is effective for angle-of-arrival estimation in the far-field and can be extended for full source localization in the near-field. However, it suffers from high computational complexity, which becomes especially prohibitive in near-field scenarios due to the need for exhaustive 3D grid searches. This paper presents a machine learning-based approach for 3D localization of near-field sources in mixed line-of-sight (LoS)/non-LoS scen…

Machine Learning with Privacy for Protected Attributes
Saeed Mahloujifar, Chuan Guo, G. Edward Suh, Kamalika Chaudhuri
https://arxiv.org/abs/2506.19836 htt…

Machine Learning with Privacy for Protected Attributes
Differential privacy (DP) has become the standard for private data analysis. Certain machine learning applications only require privacy protection for specific protected attributes. Using naive variants of differential privacy in such use cases can result in unnecessary degradation of utility. In this work, we refine the definition of DP to create a more general and flexible framework that we call feature differential privacy (FDP). Our definition is simulation-based and allows for both additio…

Supervised Similarity for Firm Linkages
Ryan Samson, Adrian Banner, Luca Candelori, Sebastien Cottrell, Tiziana Di Matteo, Paul Duchnowski, Vahagn Kirakosyan, Jose Marques, Kharen Musaelian, Stefano Pasquali, Ryan Stever, Dario Villani
https://arxiv.org/abs/2506.19856

Supervised Similarity for Firm Linkages
We introduce a novel proxy for firm linkages, Characteristic Vector Linkages (CVLs). We use this concept to estimate firm linkages, first through Euclidean similarity, and then by applying Quantum Cognition Machine Learning (QCML) to similarity learning. We demonstrate that both methods can be used to construct profitable momentum spillover trading strategies, but QCML similarity outperforms the simpler Euclidean similarity.

Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities
Soojin Park, Su Yeon Kim, Xinyao Zheng, Chioun Lee
https://arxiv.org/abs/2506.18994

Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities
Educational disparities are rooted in and perpetuate social inequalities across multiple dimensions such as race, socioeconomic status, and geography. To reduce disparities, most intervention strategies focus on a single domain and frequently evaluate their effectiveness by using causal decomposition analysis. However, a growing body of research suggests that single-domain interventions may be insufficient for individuals marginalized on multiple fronts. While interventions across multiple doma…

Piecewise Linear Approximation in Learned Index Structures: Theoretical and Empirical Analysis
Jiayong Qin, Xianyu Zhu, Qiyu Liu, Guangyi Zhang, Zhigang Cai, Jianwei Liao, Sha Hu, Jingshu Peng, Yingxia Shao, Lei Chen
https://arxiv.org/abs/2506.20139

Piecewise Linear Approximation in Learned Index Structures: Theoretical and Empirical Analysis
A growing trend in the database and system communities is to augment conventional index structures, such as B+-trees, with machine learning (ML) models. Among these, error-bounded Piecewise Linear Approximation ($ε$-PLA) has emerged as a popular choice due to its simplicity and effectiveness. Despite its central role in many learned indexes, the design and analysis of $ε$-PLA fitting algorithms remain underexplored. In this paper, we revisit $ε$-PLA from both theoretical and empirical perspe…

Comparative analysis of machine learning techniques for feature selection and classification of Fast Radio Bursts
Ailton J. B. J\'unior, J\'eferson A. S. Fortunato, Leonardo J. Silvestre, Thonimar V. Alencar, Wiliam S. Hip\'olito-Ricaldi
https://arxiv.org/abs/2506.18854

Comparative analysis of machine learning techniques for feature selection and classification of Fast Radio Bursts
Fast Radio Bursts (FRBs) are millisecond-duration radio transients of extragalactic origin, exhibiting a wide range of physical and observational properties. Distinguishing between repeating and non-repeating FRBs remains a key challenge in understanding their nature. In this work, we apply unsupervised machine learning techniques to classify FRBs based on both primary observables from the CHIME catalog and physically motivated derived features. We evaluate three hybrid pipelines combining dime…

HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation
R. Alfaro, C. Alvarez, A. Andr\'es, E. Anita-Rangel, M. Araya, J. C. Arteaga-Vel\'azquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, P. Bangale, E. Belmont-Moreno, A. Bernal, T. Capistr\'an, A. Carrami\~nana, F. Carre\'on, S. Casanova, U. Cotti, E. De la Fuente, D. Depaoli, P. Desiati, N. Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. D\'iaz-V\'elez, K. Engel, T. Ergin, C. Espinoza,…

HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation
Improving gamma-hadron separation is one of the most effective ways to enhance the performance of ground-based gamma-ray observatories. With over a decade of continuous operation, the High-Altitude Water Cherenkov (HAWC) Observatory has contributed significantly to high-energy astrophysics. To further leverage its rich dataset, we introduce a machine learning approach for gamma-hadron separation. A Multilayer Perceptron shows the best performance, surpassing traditional and other Machine Learni…

Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Zachary D King, Maryam Khalid, Han Yu, Kei Shibuya, Khadija Zanna, Marzieh Majd, Ryan L Brown, Yufei Shen, Thomas Vaessen, George Kypriotakis, Christopher P Fagundes, Akane Sano
https://arxiv.org/abs/2506.15834

Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Mobile health (mHealth) systems help researchers monitor and care for patients in real-world settings. Studies utilizing mHealth applications use Ecological Momentary Assessment (EMAs), passive sensing, and contextual features to develop emotion recognition models, which rely on EMA responses as ground truth. Due to this, it is crucial to consider EMA compliance when conducting a successful mHealth study. Utilizing machine learning is one approach that can solve this problem by sending EMAs bas…

Modeling phase transformations in Mn-rich disordered rocksalt cathodes with charge-informed machine-learning interatomic potentials
Peichen Zhong, Bowen Deng, Shashwat Anand, Tara Mishra, Gerbrand Ceder
https://arxiv.org/abs/2506.20605

Modeling phase transformations in Mn-rich disordered rocksalt cathodes with charge-informed machine-learning interatomic potentials
Mn-rich disordered rocksalt (DRX) cathode materials exhibit a phase transformation from a disordered to a partially disordered spinel-like structure ($δ$-phase) during electrochemical cycling. In this computational study, we used charge-informed molecular dynamics with a fine-tuned CHGNet foundation potential to investigate the phase transformation in Li$_{x}$Mn$_{0.8}$Ti$_{0.1}$O$_{1.9}$F$_{0.1}$. Our results indicate that transition metal migration occurs and reorders to form the spinel-like…

Advanced For-Loop for QML algorithm search
FuTe Wong
https://arxiv.org/abs/2506.18260 https://arxiv.org/pdf/2506.18260

Advanced For-Loop for QML algorithm search
This paper introduces an advanced framework leveraging Large Language Model-based Multi-Agent Systems (LLMMA) for the automated search and optimization of Quantum Machine Learning (QML) algorithms. Inspired by Google DeepMind's FunSearch, the proposed system works on abstract level to iteratively generates and refines quantum transformations of classical machine learning algorithms (concepts), such as the Multi-Layer Perceptron, forward-forward and backpropagation algorithms. As a proof of conc…

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

Smart Cuts: Enhance Active Learning for Vulnerability Detection by Pruning Bad Seeds
Xiang Lan, Tim Menzies, Bowen Xu
https://arxiv.org/abs/2506.20444 http…

Smart Cuts: Enhance Active Learning for Vulnerability Detection by Pruning Bad Seeds
Vulnerability detection is crucial for identifying security weaknesses in software systems. However, the effectiveness of machine learning models in this domain is often hindered by low-quality training datasets, which contain noisy, mislabeled, or imbalanced samples. This paper proposes a novel dataset maps-empowered approach that systematically identifies and mitigates hard-to-learn outliers, referred to as "bad seeds", to improve model training efficiency. Our approach can categorize trainin…

The Persistent Effects of Peru's Mining MITA: Double Machine Learning Approach
Alper Deniz Karakas
https://arxiv.org/abs/2506.18947 https://

The Persistent Effects of Peru's Mining MITA: Double Machine Learning Approach
This study examines the long-term economic impact of the colonial Mita system in Peru, building on Melissa Dell's foundational work on the enduring effects of forced labor institutions. The Mita, imposed by the Spanish colonial authorities from 1573 to 1812, required indigenous communities within a designated boundary to supply labor to mines, primarily near Potosi. Dell's original regression discontinuity design (RDD) analysis, leveraging the Mita boundary to estimate the Mita's legacy on mode…

This https://arxiv.org/abs/2504.16152 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Heterogeneous networks in drug-target interaction prediction
Drug discovery requires a tremendous amount of time and cost. Computational drug-target interaction prediction, a significant part of this process, can reduce these requirements by narrowing the search space for wet lab experiments. In this survey, we provide comprehensive details of graph machine learning-based methods in predicting drug-target interaction, as they have shown promising results in this field. These details include the overall framework, main contribution, datasets, and their so…

Generative Machine-Learning-Systeme überfordern uns alle - wir sollten das auch zugeben.
Tom Davenport, x-facher Buchautor und Wissensmanagement-Experte meinte, dass er trotz Verfassen von 2 Büchern zum Thema keine Ahnung habe, wie es bezüglich GMLS weitergehen werde.
https://blog.doeb…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
Kelvinius, Andersson, Parackal, Qian, Armiento, Lindsten

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

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

DPG loss functions for learning parameter-to-solution maps by neural networks
Pablo Cort\'es Castillo, Wolfgang Dahmen, Jay Gopalakrishnan
https://arxiv.org/abs/2506.18773

DPG loss functions for learning parameter-to-solution maps by neural networks
We develop, analyze, and experimentally explore residual-based loss functions for machine learning of parameter-to-solution maps in the context of parameter-dependent families of partial differential equations (PDEs). Our primary concern is on rigorous accuracy certification to enhance prediction capability of resulting deep neural network reduced models. This is achieved by the use of variationally correct loss functions. Through one specific example of an elliptic PDE, details for establishin…

Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards
Wenlong Ji, Yihan Pan, Ruihao Zhu, Lihua Lei
https://arxiv.org/abs/2506.16658 https:/…

Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards
Multi-armed bandit (MAB) is a widely adopted framework for sequential decision-making under uncertainty. Traditional bandit algorithms rely solely on online data, which tends to be scarce as it must be gathered during the online phase when the arms are actively pulled. However, in many practical settings, rich auxiliary data, such as covariates of past users, is available prior to deploying any arms. We introduce a new setting for MAB where pre-trained machine learning (ML) models are applied t…

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

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Flexible Infinite-Width Graph Convolutional Neural Networks
Ben Anson, Edward Milsom, Laurence Aitchison

Transformer Based Multi-Target Bernoulli Tracking for Maritime Radar
Caden Sweeney, Du Yong Kim, Branko Ristic, Brian Cheung
https://arxiv.org/abs/2506.20319

Transformer Based Multi-Target Bernoulli Tracking for Maritime Radar
Multi-target tracking in the maritime domain is a challenging problem due to the non-Gaussian and fluctuating characteristics of sea clutter. This article investigates the use of machine learning (ML) to the detection and tracking of low SIR targets in the maritime domain. The proposed method uses a transformer to extract point measurements from range-azimuth maps, before clustering and tracking using the Labelled mulit- Bernoulli (LMB) filter. A measurement driven birth density design based on…

Analysis of Photonic Circuit Losses with Machine Learning Techniques
Adrian Nugraha Utama, Simon Chun Kiat Goh, Li Hongyu, Wang Xiangyu, Zhou Yanyan, Victor Leong, Manas Mukherjee
https://arxiv.org/abs/2506.17999

Analysis of Photonic Circuit Losses with Machine Learning Techniques
Low-loss waveguides enable efficient light delivery in photonic circuits, which are essential for high-speed optical communications and scalable implementations of photonic quantum technologies. We study the effects of several fabrication and experimental parameters on the waveguide losses of a silicon nitride integrated photonics platform using various machine learning techniques. Compared to more complex machine learning algorithms, our results show that a simple linear regression model with …

Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
Arslan Mazitov, Sofiia Chorna, Guillaume Fraux, Marnik Bercx, Giovanni Pizzi, Sandip De, Michele Ceriotti
https://arxiv.org/abs/2506.19674

Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
The development of machine-learning models for atomic-scale simulations has benefited tremendously from the large databases of materials and molecular properties computed in the past two decades using electronic-structure calculations. More recently, these databases have made it possible to train universal models that aim at making accurate predictions for arbitrary atomic geometries and compositions. The construction of many of these databases was however in itself aimed at materials discovery…

Amplifying Machine Learning Attacks Through Strategic Compositions
Yugeng Liu, Zheng Li, Hai Huang, Michael Backes, Yang Zhang
https://arxiv.org/abs/2506.18870

Amplifying Machine Learning Attacks Through Strategic Compositions
Machine learning (ML) models are proving to be vulnerable to a variety of attacks that allow the adversary to learn sensitive information, cause mispredictions, and more. While these attacks have been extensively studied, current research predominantly focuses on analyzing each attack type individually. In practice, however, adversaries may employ multiple attack strategies simultaneously rather than relying on a single approach. This prompts a crucial yet underexplored question: When the adver…

Conservative quantum offline model-based optimization
Kristian Sotirov, Annie E. Paine, Savvas Varsamopoulos, Antonio A. Gentile, Osvaldo Simeone
https://arxiv.org/abs/2506.19714 …

Conservative quantum offline model-based optimization
Offline model-based optimization (MBO) refers to the task of optimizing a black-box objective function using only a fixed set of prior input-output data, without any active experimentation. Recent work has introduced quantum extremal learning (QEL), which leverages the expressive power of variational quantum circuits to learn accurate surrogate functions by training on a few data points. However, as widely studied in the classical machine learning literature, predictive models may incorrectly e…

NetSenseML: Network-Adaptive Compression for Efficient Distributed Machine Learning
Yisu Wang, Xinjiao Li, Ruilong Wu, Huangxun Chen, Dirk Kutscher
https://arxiv.org/abs/2506.16235

NetSenseML: Network-Adaptive Compression for Efficient Distributed Machine Learning
Training large-scale distributed machine learning models imposes considerable demands on network infrastructure, often resulting in sudden traffic spikes that lead to congestion, increased latency, and reduced throughput, which would ultimately affect convergence times and overall training performance. While gradient compression techniques are commonly employed to alleviate network load, they frequently compromise model accuracy due to the loss of gradient information. This paper introduces N…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- Supporting renewable energy planning and operation with data-driven high-resolution ensemble weat...
Wang, Chao, Yang, Nai, Ren, Deng, Chen, Liu, Wen, Xiao, Zhang, Wang, Guan, Pan

A Neural-Operator Surrogate for Platelet Deformation Across Capillary Numbers
Marco Laudato
https://arxiv.org/abs/2506.20341 https://…

A Neural-Operator Surrogate for Platelet Deformation Across Capillary Numbers
Reliable multiscale models of thrombosis require platelet-scale fidelity at organ-scale cost, a gap that scientific machine learning has the potential to narrow. We train a DeepONet surrogate on platelet dynamics generated with LAMMPS for platelets spanning ten elastic moduli and capillary numbers (0.07 - 0.77). The network takes in input the wall shear stress, bond stiffness, time, and initial particle coordinates and returns the full three-dimensional deformation of the membrane. Mean-squared…

Causality in the human niche: lessons for machine learning
Richard D. Lange, Konrad P. Kording
https://arxiv.org/abs/2506.13803 https://

Causality in the human niche: lessons for machine learning
Humans interpret the world around them in terms of cause and effect and communicate their understanding of the world to each other in causal terms. These causal aspects of human cognition are thought to underlie humans' ability to generalize and learn efficiently in new domains, an area where current machine learning systems are weak. Building human-like causal competency into machine learning systems may facilitate the construction of effective and interpretable AI. Indeed, the machine learnin…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- Backpropagation Through Time For Networks With Long-Term Dependencies
George Bird, Maxim E. Polivoda

Modern approaches to building effective interpretable models of the property market using machine learning
Irina G. Tanashkina, Alexey S. Tanashkin, Alexander S. Maksimchuik, Anna Yu. Poshivailo
https://arxiv.org/abs/2506.15723

Modern approaches to building effective interpretable models of the property market using machine learning
In this article, we review modern approaches to building interpretable models of property markets using machine learning on the base of mass valuation of property in the Primorye region, Russia. The researcher, lacking expertise in this topic, encounters numerous difficulties in the effort to build a good model. The main source of this is the huge difference between noisy real market data and ideal data which is very common in all types of tutorials on machine learning. This paper covers all st…

Which Company Adjustment Matter? Insights from Uplift Modeling on Financial Health
Xinlin Wang, Mats Brorsson
https://arxiv.org/abs/2506.19049 https://

Which Company Adjustment Matter? Insights from Uplift Modeling on Financial Health
Uplift modeling has achieved significant success in various fields, particularly in online marketing. It is a method that primarily utilizes machine learning and deep learning to estimate individual treatment effects. This paper we apply uplift modeling to analyze the effect of company adjustment on their financial status, and we treat these adjustment as treatments or interventions in this study. Although there have been extensive studies and application regarding binary treatments, multiple t…

[2025-06-26 Thu (UTC), 8 new articles found for stat.ML Machine Learning]
toXiv_bot_toot

A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset
Rachel Hong, Jevan Hutson, William Agnew, Imaad Huda, Tadayoshi Kohno, Jamie Morgenstern
https://arxiv.org/abs/2506.17185

A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset
We investigate the contents of web-scraped data for training AI systems, at sizes where human dataset curators and compilers no longer manually annotate every sample. Building off of prior privacy concerns in machine learning models, we ask: What are the legal privacy implications of web-scraped machine learning datasets? In an empirical study of a popular training dataset, we find significant presence of personally identifiable information despite sanitization efforts. Our audit provides concr…

Dynamics of tidal spiral arms: Machine learning-assisted identification of equations and application to the Milky Way
Marcel Bernet, Pau Ramos, Teresa Antoja, Adrian Price-Whelan, Steven L. Brunton, Tetsuro Asano, Alexandra Gir\'on-Soto
https://arxiv.org/abs/2506.17383

Dynamics of tidal spiral arms: Machine learning-assisted identification of equations and application to the Milky Way
Understanding the spiral arms of the Milky Way (MW) remains a key open question in galactic dynamics. Tidal perturbations, such as the recent passage of the Sagittarius dwarf galaxy (Sgr), could play a significant role in exciting them. We aim to analytically characterize the dynamics of tidally induced spiral arms, including their phase-space signatures. We ran idealized test-particle simulations resembling impulsive satellite impacts, and used the Sparse Identification of Non-linear Dynamics …

Machine Learning Accelerates Raman Computations from Molecular Dynamics for Materials Science
David A. Egger, Manuel Grumet, Tom\'a\v{s} Bu\v{c}ko
https://arxiv.org/abs/2506.19595

Machine Learning Accelerates Raman Computations from Molecular Dynamics for Materials Science
Raman spectroscopy is a powerful experimental technique for characterizing molecules and materials that is used in many laboratories. First-principles theoretical calculations of Raman spectra are important because they elucidate the microscopic effects underlying Raman activity in these systems. These calculations are often performed using the canonical harmonic approximation which cannot capture certain thermal changes in the Raman response. Anharmonic vibrational effects were recently found …

Global Convergence of Iteratively Reweighted Least Squares for Robust Subspace Recovery
Gilad Lerman, Kang Li, Tyler Maunu, Teng Zhang
https://arxiv.org/abs/2506.20533

Global Convergence of Iteratively Reweighted Least Squares for Robust Subspace Recovery
Robust subspace estimation is fundamental to many machine learning and data analysis tasks. Iteratively Reweighted Least Squares (IRLS) is an elegant and empirically effective approach to this problem, yet its theoretical properties remain poorly understood. This paper establishes that, under deterministic conditions, a variant of IRLS with dynamic smoothing regularization converges linearly to the underlying subspace from any initialization. We extend these guarantees to affine subspace estima…

How Do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?
Shamse Tasnim Cynthia, Nuri Almarimi, Banani Roy
https://arxiv.org/abs/2506.15884

How Do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?
Community smells reflect poor organizational practices that often lead to socio-technical issues and the accumulation of Self-Admitted Technical Debt (SATD). While prior studies have explored these problems in general software systems, their interplay in machine learning (ML)-based projects remains largely underexamined. In this study, we investigated the prevalence of community smells and their relationship with SATD in open-source ML projects, analyzing data at the release level. First, we ex…

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network
Yining Pang, Chenghan Li
https://arxiv.org/abs/2506.19871

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network
Insurance fraud detection represents a pivotal advancement in modern insurance service, providing intelligent and digitalized monitoring to enhance management and prevent fraud. It is crucial for ensuring the security and efficiency of insurance systems. Although AI and machine learning algorithms have demonstrated strong performance in detecting fraudulent claims, the absence of standardized defense mechanisms renders current systems vulnerable to emerging adversarial threats. In this paper, w…

Comparative analysis of financial data differentiation techniques using LSTM neural network
Dominik Stempie\'n, Janusz Gajda
https://arxiv.org/abs/2505.19243

Comparative analysis of financial data differentiation techniques using LSTM neural network
We compare traditional approach of computing logarithmic returns with the fractional differencing method and its tempered extension as methods of data preparation before their usage in advanced machine learning models. Differencing parameters are estimated using multiple techniques. The empirical investigation is conducted on data from four major stock indices covering the most recent 10-year period. The set of explanatory variables is additionally extended with technical indicators. The effect…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention
Ayush K. Varshney, Vicen\c{c} Torra

Enhanced Image Recognition Using Gaussian Boson Sampling
Si-Qiu Gong, Ming-Cheng Chen, Hua-Liang Liu, Hao Su, Yi-Chao Gu, Hao-Yang Tang, Meng-Hao Jia, Yu-Hao Deng, Qian Wei, Hui Wang, Han-Sen Zhong, Xiao Jiang, Li Li, Nai-Le Liu, Chao-Yang Lu, Jian-Wei Pan
https://arxiv.org/abs/2506.19707

Enhanced Image Recognition Using Gaussian Boson Sampling
Gaussian boson sampling (GBS) has emerged as a promising quantum computing paradigm, demonstrating its potential in various applications. However, most existing works focus on theoretical aspects or simple tasks, with limited exploration of its capabilities in solving real-world practical problems. In this work, we propose a novel GBS-based image recognition scheme inspired by extreme learning machine (ELM) to enhance the performance of perceptron and implement it using our latest GBS device, J…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification
Renato De Leone, Francesca Maggioni, Andrea Spinelli

Reducing Self-Interaction Error in Transition-Metal Oxides with Different Exact-Exchange Fractions for Energy and Density
Harshan Reddy Gopidi, Ruiqi Zhang, Yanyong Wang, Abhirup Patra, Jianwei Sun, Adrienn Ruzsinszky, John P. Perdew, Pieremanuele Canepa
https://arxiv.org/abs/2506.20635 …

Reducing Self-Interaction Error in Transition-Metal Oxides with Different Exact-Exchange Fractions for Energy and Density
DFT is vital for materials discovery, and at the base of extensive molecular and materials databases, chemical reaction predictions, and machine learning potentials. The widespread use of DFT in chemistry and materials science aims for "chemical accuracy," but this is limited by the unknown exchange and correlation (XC) functional. A meta-GGA, the restored regularized strongly constrained and appropriately normed, r r2SCAN XC functional, fulfils 17 exact constraints of the XC energy. r2SCAN sti…

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
Bishwajit Prasad Gond, Rajneekant, Pushkar Kishore, Durga Prasad Mohapatra
https://arxiv.org/abs/2506.16224

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
This paper investigates the application of natural language processing (NLP)-based n-gram analysis and machine learning techniques to enhance malware classification. We explore how NLP can be used to extract and analyze textual features from malware samples through n-grams, contiguous string or API call sequences. This approach effectively captures distinctive linguistic patterns among malware and benign families, enabling finer-grained classification. We delve into n-gram size selection, featu…

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

Diverse polymorphs and phase transitions in van der Waals In$_2$Se$_3$
Mingfeng Liu, Jiantao Wang, Peitao Liu, Qiang Wang, Zhibo Liu, Yan Sun, Xing-Qiu Chen
https://arxiv.org/abs/2506.21248

Diverse polymorphs and phase transitions in van der Waals In$_2$Se$_3$
Van der Waals In$_2$Se$_3$ has garnered significant attention due to its unique properties and wide applications associated with its rich polymorphs and polymorphic phase transitions. Despite extensive studies, the vast complex polymorphic phase space remains largely unexplored, and the underlying microscopic mechanism for their phase transformations remains elusive. Here, we develop a highly accurate, efficient, and reliable machine-learning potential (MLP), which not only facilitates accurate…

Trustworthy Artificial Intelligence for Cyber Threat Analysis
Shuangbao Paul Wang, Paul Mullin
https://arxiv.org/abs/2506.19052 https://

Trustworthy Artificial Intelligence for Cyber Threat Analysis
Artificial Intelligence brings innovations into the society. However, bias and unethical exist in many algorithms that make the applications less trustworthy. Threats hunting algorithms based on machine learning have shown great advantage over classical methods. Reinforcement learning models are getting more accurate for identifying not only signature-based but also behavior-based threats. Quantum mechanics brings a new dimension in improving classification speed with exponential advantage. In …

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- Controllable Video Generation with Provable Disentanglement
Shen, Zhu, Li, Xie, Tang, Deka, Liu, Chen, Zhang

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Iterative Minimax Games with Coupled Linear Constraints
Huiling Zhang, Zi Xu, Yu-Hong Dai

Leveraging Transfer Learning to Overcome Data Limitations in Czochralski Crystal Growth
Milena Petkovic, Natasha Dropka, Xia Tang, Janina Zittel
https://arxiv.org/abs/2506.18774

Leveraging Transfer Learning to Overcome Data Limitations in Czochralski Crystal Growth
The Czochralski (Cz) method is a widely used process for growing high-quality single crystals, critical for applications in semiconductors, optics, and advanced materials. Achieving optimal growth conditions requires precise control of process and furnace design parameters. Still, data scarcity -- especially for new materials -- limits the application of machine learning (ML) in predictive modeling and optimization. This study proposes a transfer learning approach to overcome this limitation by…

[2025-06-25 Wed (UTC), 7 new articles found for stat.ML Machine Learning]
toXiv_bot_toot

Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy
Siya Zhu, Doguhan Sariturk, Raymundo Arroyave
https://arxiv.org/abs/2506.16771

Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy
High-entropy alloys (HEAs) have attracted increasing attention due to their unique structural and functional properties. In the study of HEAs, thermodynamic properties and phase stability play a crucial role, making phase diagram calculations significantly important. However, phase diagram calculations with conventional CALPHAD assessments based on experimental or ab-initio data can be expensive. With the emergence of machine-learning interatomic potentials (MLIPs), we have developed a program …

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/11]:
- Navigating Conflicting Views: Harnessing Trust for Learning
Jueqing Lu, Wray Buntine, Yuanyuan Qi, Joanna Dipnall, Belinda Gabbe, Lan Du

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…

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials
Minseok Moon, Seungwoo Hwang, Jaesun Kim, Yutack Park, Changho Hong, Seungwu Han
https://arxiv.org/abs/2506.15934

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials
Ovonic threshold switching (OTS) selectors play a critical role in non-volatile memory devices because of their nonlinear electrical behavior and polarity-dependent threshold voltages. However, the atomic-scale origins of the defect states responsible for these properties are not yet fully understood. In this study, we use molecular dynamics simulations accelerated by machine-learning interatomic potentials to investigate defects in amorphous GeSe. We begin by benchmarking several potential arc…

[2025-06-25 Wed (UTC), 77 new articles found for cs.LG Machine Learning]
toXiv_bot_toot

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[11/11]:
- Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings
Aditya Sengar, Ali Hariri, Daniel Probst, Patrick Barth, Pierre Vandergheynst

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[10/11]:
- FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answ...
Chengyue Huang, Brisa Maneechotesuwan, Shivang Chopra, Zsolt Kira

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[9/11]:
- Cramming 1568 Tokens into a Single Vector and Back Again: Exploring the Limits of Embedding Space...
Yuri Kuratov, Mikhail Arkhipov, Aydar Bulatov, Mikhail Burtsev

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[8/11]:
- Radio Map Prediction from Aerial Images and Application to Coverage Optimization
Fabian Jaensch, Giuseppe Caire, Beg\"um Demir

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/11]:
- Disentangling representations of retinal images with generative models
Sarah M\"uller, Lisa M. Koch, Hendrik P. A. Lensch, Philipp Berens

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/11]:
- Bohdi: Heterogeneous LLM Fusion with Automatic Data Exploration
Junqi Gao, Zhichang Guo, Dazhi Zhang, Dong Li, Runze Liu, Pengfei Li, Kai Tian, Biqing Qi

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/11]:
- Memorization to Generalization: Emergence of Diffusion Models from Associative Memory
Bao Pham, Gabriel Raya, Matteo Negri, Mohammed J. Zaki, Luca Ambrogioni, Dmitry Krotov

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/11]:
- AutoPDL: Automatic Prompt Optimization for LLM Agents
Claudio Spiess, Mandana Vaziri, Louis Mandel, Martin Hirzel

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/11]:
- One-Step is Enough: Sparse Autoencoders for Text-to-Image Diffusion Models
Surkov, Wendler, Mari, Terekhov, Deschenaux, West, Gulcehre, Bau

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/11]:
- On the fast convergence of minibatch heavy ball momentum
Raghu Bollapragada, Tyler Chen, Rachel Ward

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[9/9]:
- Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners
Drashthi Doshi, Aditya Vema Reddy Kesari, Swaprava Nath, Avishek Ghosh, Suhas S Kowshik

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/9]:
- Eau De $Q$-Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning
Th\'eo Vincent, Tim Faust, Yogesh Tripathi, Jan Peters, Carlo D'Eramo

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/9]:
- Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning
Zhao, Zhou, Zhang, Zhu, Shen, Li, Yang, Wang, Su, Kuang, Wei, Wu, Cheng