Tootfinder

From Learning to Mastery: Achieving Safe and Efficient Real-World Autonomous Driving with Human-In-The-Loop Reinforcement Learning
Li Zeqiao, Wang Yijing, Wang Haoyu, Li Zheng, Li Peng, Liu Wenfei, Zuo Zhiqiang
https://arxiv.org/abs/2510.06038

From Learning to Mastery: Achieving Safe and Efficient Real-World Autonomous Driving with Human-In-The-Loop Reinforcement Learning
Autonomous driving with reinforcement learning (RL) has significant potential. However, applying RL in real-world settings remains challenging due to the need for safe, efficient, and robust learning. Incorporating human expertise into the learning process can help overcome these challenges by reducing risky exploration and improving sample efficiency. In this work, we propose a reward-free, active human-in-the-loop learning method called Human-Guided Distributional Soft Actor-Critic (H-DSAC). …

R learning for applied statistics by Chenxin Li: #rstats

GitHub - cxli233/Online_R_learning: Online R learning for applied statistics
Online R learning for applied statistics . Contribute to cxli233/Online_R_learning development by creating an account on GitHub.

Covert Quantum Learning: Privately and Verifiably Learning from Quantum Data
Abhishek Anand, Matthias C. Caro, Ari Karchmer, Saachi Mutreja
https://arxiv.org/abs/2510.07193 http…

Covert Quantum Learning: Privately and Verifiably Learning from Quantum Data
Quantum learning from remotely accessed quantum compute and data must address two key challenges: verifying the correctness of data and ensuring the privacy of the learner's data-collection strategies and resulting conclusions. The covert (verifiable) learning model of Canetti and Karchmer (TCC 2021) provides a framework for endowing classical learning algorithms with such guarantees. In this work, we propose models of covert verifiable learning in quantum learning theory and realize them witho…

Fine-Grained Emotion Recognition via In-Context Learning
Zhaochun Ren, Zhou Yang, Chenglong Ye, Haizhou Sun, Chao Chen, Xiaofei Zhu, Xiangwen Liao
https://arxiv.org/abs/2510.06600

Fine-Grained Emotion Recognition via In-Context Learning
Fine-grained emotion recognition aims to identify the emotional type in queries through reasoning and decision-making processes, playing a crucial role in various systems. Recent methods use In-Context Learning (ICL), enhancing the representation of queries in the reasoning process through semantically similar examples, while further improving emotion recognition by explaining the reasoning mechanisms. However, these methods enhance the reasoning process but overlook the decision-making process…

In a scorching 46-page opinion, Judge Boasberg found probable cause to hold the government in criminal contempt.
The government could remedy the contempt, Boasberg said, by giving the men detained under the Alien Enemies Act an opportunity to challenge their detentions.
If not, he would initiate proceedings to figure out who was responsible for the contempt.
And, if the Justice Department ultimately declined to charge that person with a crime, Boasberg said that he’d appoin…

SL-SLR: Self-Supervised Representation Learning for Sign Language Recognition
Ariel Basso Madjoukeng, J\'er\^ome Fink, Pierre Poitier, Edith Belise Kenmogne, Benoit Frenay
https://arxiv.org/abs/2509.05188

SL-SLR: Self-Supervised Representation Learning for Sign Language Recognition
Sign language recognition (SLR) is a machine learning task aiming to identify signs in videos. Due to the scarcity of annotated data, unsupervised methods like contrastive learning have become promising in this field. They learn meaningful representations by pulling positive pairs (two augmented versions of the same instance) closer and pushing negative pairs (different from the positive pairs) apart. In SLR, in a sign video, only certain parts provide information that is truly useful for its r…

Chrysalis: A Unified System for Comparing Active Teaching and Passive Learning with AI Agents in Education
Prashanth Arun, Vinita Vader, Erya Xu, Brent McCready-Branch, Sarah Seabrook, Kyle Scholz, Ana Crisan, Igor Grossmann, Pascal Poupart
https://arxiv.org/abs/2510.05271

Chrysalis: A Unified System for Comparing Active Teaching and Passive Learning with AI Agents in Education
AI-assisted learning has seen a remarkable uptick over the last few years, mainly due to the rise in popularity of Large Language Models (LLMs). Their ability to hold long-form, natural language interactions with users makes them excellent resources for exploring school- and university-level topics in a dynamic, active manner. We compare students' experiences when interacting with an LLM companion in two capacities: tutored learning and learning-by-teaching. We do this using Chrysalis, an LLM-b…

On the Interplay of Cube Learning and Dependency Schemes in QCDCL Proof Systems
Abhimanyu Choudhury, Meena Mahajan
https://arxiv.org/abs/2510.05876 https://

On the Interplay of Cube Learning and Dependency Schemes in QCDCL Proof Systems
Quantified Conflict Driven Clause Leaning (QCDCL) is one of the main approaches to solving Quantified Boolean Formulas (QBF). Cube-learning is employed in this approach to ensure that true formulas can be verified. Dependency Schemes help to detect spurious dependencies that are implied by the variable ordering in the quantifier prefix of QBFs but are not essential for constructing (counter)models. This detection can provably shorten refutations in specific proof systems, and is expected to spe…

Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions
Wanli Ni, Hui Tian, Shuai Wang, Chengyang Li, Lei Sun, Zhaohui Yang
https://arxiv.org/abs/2510.05713

Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions
Federated split learning (FedSL) has emerged as a promising paradigm for enabling collaborative intelligence in industrial Internet of Things (IoT) systems, particularly in smart factories where data privacy, communication efficiency, and device heterogeneity are critical concerns. In this article, we present a comprehensive study of FedSL frameworks tailored for resource-constrained robots in industrial scenarios. We compare synchronous, asynchronous, hierarchical, and heterogeneous FedSL fram…

Refereed Learning
Ran Canetti, Ephraim Linder, Connor Wagaman
https://arxiv.org/abs/2510.05440 https://arxiv.org/pdf/2510.05440

Refereed Learning
We initiate an investigation of learning tasks in a setting where the learner is given access to two competing provers, only one of which is honest. Specifically, we consider the power of such learners in assessing purported properties of opaque models. Following prior work that considers the power of competing provers in different settings, we call this setting refereed learning. After formulating a general definition of refereed learning tasks, we show refereed learning protocols that obtai…

MachineLearningLM: Continued Pretraining Language Models on Millions of Synthetic Tabular Prediction Tasks Scales In-Context ML
Haoyu Dong, Pengkun Zhang, Mingzhe Lu, Yanzhen Shen, Guolin Ke
https://arxiv.org/abs/2509.06806

MachineLearningLM: Continued Pretraining Language Models on Millions of Synthetic Tabular Prediction Tasks Scales In-Context ML
Large language models (LLMs) possess broad world knowledge and strong general-purpose reasoning ability, yet they struggle to learn from many in-context examples on standard machine learning (ML) tasks, that is, to leverage many-shot demonstrations purely via in-context learning (ICL) without gradient descent. We introduce MachineLearningLM, a portable continued-pretraining framework that equips a general-purpose LLM with robust in-context ML capability while preserving its general knowledge an…

Impact of Labeling Inaccuracy and Image Noise on Tooth Segmentation in Panoramic Radiographs using Federated, Centralized and Local Learning
Johan Andreas Balle Rubak, Khuram Naveed, Sanyam Jain, Lukas Esterle, Alexandros Iosifidis, Ruben Pauwels
https://arxiv.org/abs/2509.06553

Impact of Labeling Inaccuracy and Image Noise on Tooth Segmentation in Panoramic Radiographs using Federated, Centralized and Local Learning
Objectives: Federated learning (FL) may mitigate privacy constraints, heterogeneous data quality, and inconsistent labeling in dental diagnostic AI. We compared FL with centralized (CL) and local learning (LL) for tooth segmentation in panoramic radiographs across multiple data corruption scenarios. Methods: An Attention U-Net was trained on 2066 radiographs from six institutions across four settings: baseline (unaltered data); label manipulation (dilated/missing annotations); image-quality man…

Students' Perception of LLM Use in Requirements Engineering Education: An Empirical Study Across Two Universities
Sharon Guardado, Risha Parveen, Zheying Zhang, Maruf Rayhan, Nirnaya Tripathi
https://arxiv.org/abs/2509.05995

Students' Perception of LLM Use in Requirements Engineering Education: An Empirical Study Across Two Universities
The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The s…

Redefining Cost Estimation in Database Systems: The Role of Execution Plan Features and Machine Learning
Utsav Pathak, Amit Mankodi
https://arxiv.org/abs/2510.05612 https://

Redefining Cost Estimation in Database Systems: The Role of Execution Plan Features and Machine Learning
Accurate query runtime prediction is a critical component of effective query optimization in modern database systems. Traditional cost models, such as those used in PostgreSQL, rely on static heuristics that often fail to reflect actual query performance under complex and evolving workloads. This remains an active area of research, with recent work exploring machine learning techniques to replace or augment traditional cost estimators. In this paper, we present a machine learning-based framewor…

Estimating Cellular Network Delays in Finnish Railways: A Machine Learning Enhanced Approach
Saeideh Mansouri, Mohamed Shamekh, Simon Indola, Petri Mahonen
https://arxiv.org/abs/2509.05003

Estimating Cellular Network Delays in Finnish Railways: A Machine Learning Enhanced Approach
There is growing interest in using public cellular networks for specialized communication applications, replacing standalone sector-specific networks. One such application is transitioning from the aging GSM-R railway network to public 4G and 5G networks. Finland is modernizing its railway communication system through the Digirail project, leveraging public cellular networks. To evaluate network performance, a nationwide measurement campaign was conducted in two modes: Best Quality and Packet R…

Bridging the Gap Between Theoretical and Practical Reinforcement Learning in Undergraduate Education
Muhammad Ahmed Atif, Mohammad Shahid Shaikh
https://arxiv.org/abs/2509.05689

Bridging the Gap Between Theoretical and Practical Reinforcement Learning in Undergraduate Education
This innovative practice category paper presents an innovative framework for teaching Reinforcement Learning (RL) at the undergraduate level. Recognizing the challenges posed by the complex theoretical foundations of the subject and the need for hands-on algorithmic practice, the proposed approach integrates traditional lectures with interactive lab-based learning. Drawing inspiration from effective pedagogical practices in computer science and engineering, the framework engages students throug…

Deep Learning-Based Multi-Factor Authentication: A Survey of Biometric and Smart Card Integration Approaches
Abdelilah Ganmati, Karim Afdel, Lahcen Koutti
https://arxiv.org/abs/2510.05163

Deep Learning-Based Multi-Factor Authentication: A Survey of Biometric and Smart Card Integration Approaches
In the era of pervasive cyber threats and exponential growth in digital services, the inadequacy of single-factor authentication has become increasingly evident. Multi-Factor Authentication (MFA), which combines knowledge-based factors (passwords, PINs), possession-based factors (smart cards, tokens), and inherence-based factors (biometric traits), has emerged as a robust defense mechanism. Recent breakthroughs in deep learning have transformed the capabilities of biometric systems, enabling hi…

Unified Representation Learning for Multi-Intent Diversity and Behavioral Uncertainty in Recommender Systems
Wei Xu, Jiasen Zheng, Junjiang Lin, Mingxuan Han, Junliang Du
https://arxiv.org/abs/2509.04694

Unified Representation Learning for Multi-Intent Diversity and Behavioral Uncertainty in Recommender Systems
This paper addresses the challenge of jointly modeling user intent diversity and behavioral uncertainty in recommender systems. A unified representation learning framework is proposed. The framework builds a multi-intent representation module and an uncertainty modeling mechanism. It extracts multi-granularity interest structures from user behavior sequences. Behavioral ambiguity and preference fluctuation are captured using Bayesian distribution modeling. In the multi-intent modeling part, the…

An Arbitration Control for an Ensemble of Diversified DQN variants in Continual Reinforcement Learning
Wonseo Jang, Dongjae Kim
https://arxiv.org/abs/2509.04815 https://

An Arbitration Control for an Ensemble of Diversified DQN variants in Continual Reinforcement Learning
Deep reinforcement learning (RL) models, despite their efficiency in learning an optimal policy in static environments, easily loses previously learned knowledge (i.e., catastrophic forgetting). It leads RL models to poor performance in continual reinforcement learning (CRL) scenarios. To address this, we present an arbitration control mechanism over an ensemble of RL agents. It is motivated by and closely aligned with how humans make decisions in a CRL context using an arbitration control of m…

Sometimes it feels like learning about how to handle dates & times in a new language is the hardest thing there is...

Convolution and Graph-based Deep Learning Approaches for Gamma/Hadron Separation in Imaging Atmospheric Cherenkov Telescopes
Abhay Mehta, Dan Parsons, Tim Lukas Holch, David Berge, Matthias Weidlich
https://arxiv.org/abs/2510.05736

Convolution and Graph-based Deep Learning Approaches for Gamma/Hadron Separation in Imaging Atmospheric Cherenkov Telescopes
The identification of $γ$-rays from the predominant hadronic-background is a key aspect in their ground-based detection using Imaging Atmospheric Cherenkov Telescopes (IACTs). While current methods are limited in their ability to exploit correlations in complex data, deep learning-based models offer a promising alternative by directly leveraging image-level information. However, several challenges involving the robustness and applicability of such models remain. Designing model architectures w…

"The website, called Fight Chat Control, was set up by Joachim, a 30-year-old software engineer living in Aalborg, Denmark. He made it after learning of a new attempt to approve a European Union proposal to fight child sexual abuse material (CSAM) — a bill seen by privacy activists as breaking encryption and leading to mass surveillance."
One-man spam campaign ravages EU ‘chat control’ bill

One-man spam campaign ravages EU ‘chat control’ bill
A software developer from Denmark is having an outsized influence on a hotly debated law to break open encrypted apps.

Towards In-Air Ultrasonic QR Codes: Deep Learning for Classification of Passive Reflector Constellations
Wouter Jansen, Jan Steckel
https://arxiv.org/abs/2509.06615 https://

Towards In-Air Ultrasonic QR Codes: Deep Learning for Classification of Passive Reflector Constellations
In environments where visual sensors falter, in-air sonar provides a reliable alternative for autonomous systems. While previous research has successfully classified individual acoustic landmarks, this paper takes a step towards increasing information capacity by introducing reflector constellations as encoded tags. Our primary contribution is a multi-label Convolutional Neural Network (CNN) designed to simultaneously identify multiple, closely spaced reflectors from a single in-air 3D sonar me…

Latent Representation Learning in Heavy-Ion Collisions with MaskPoint Transformer
Jing-Zong Zhang, Shuang Guo, Li-Lin Zhu, Lingxiao Wang, Guo-Liang Ma
https://arxiv.org/abs/2510.06691

Latent Representation Learning in Heavy-Ion Collisions with MaskPoint Transformer
A central challenge in high-energy nuclear physics is to extract informative features from the high-dimensional final-state data of heavy-ion collisions (HIC) in order to enable reliable downstream analyses. Traditional approaches often rely on selected observables, which may miss subtle but physically relevant structures in the data. To address this, we introduce a Transformer-based autoencoder trained with a two-stage paradigm: self-supervised pre-training followed by supervised fine-tuning. …

Now at #ISSEP2025 in Trier, waiting for Cynthia Solomons keynote "From Logo to TurtleStitch - Computers as Expressive Learning Environments"
https://issep2025.uni-trier.de/index.php

Comparison of Photometric and Spectroscopic Labels in Classifying Dusty Stellar Sources Using Machine Learning in the Magellanic Clouds
Sepideh Ghaziasgar, Mahdi Abdollahi, Atefeh Javadi, Jacco Th. van Loon, Iain McDonald, Joana Oliveira, Habib G. Khosroshahi
https://arxiv.org/abs/2509.05531

Comparison of Photometric and Spectroscopic Labels in Classifying Dusty Stellar Sources Using Machine Learning in the Magellanic Clouds
Dusty stellar sources, including young stellar objects (YSOs) and evolved stars such as oxygen- and carbon-rich AGBs (OAGBs, CAGBs), red supergiants (RSGs), and post-AGB stars (PAGBs), play a key role in the chemical enrichment of galaxies. Photometric surveys in the Magellanic Clouds have cataloged many such objects, but their classifications are often uncertain due to overlaps between populations. We trained machine learning models on spectroscopically labeled data from the SAGE project and a…

Label-frugal satellite image change detection with generative virtual exemplar learning
Hichem Sahbi
https://arxiv.org/abs/2510.06926 https://arxiv.org/pdf…

Label-frugal satellite image change detection with generative virtual exemplar learning
Change detection is a major task in remote sensing which consists in finding all the occurrences of changes in multi-temporal satellite or aerial images. The success of existing methods, and particularly deep learning ones, is tributary to the availability of hand-labeled training data that capture the acquisition conditions and the subjectivity of the user (oracle). In this paper, we devise a novel change detection algorithm, based on active learning. The main contribution of our work resides …

Lyapunov-Based Deep Learning Control for Robots with Unknown Jacobian
Koji Matsuno, Chien Chern Cheah
https://arxiv.org/abs/2509.04984 https://arxiv.org/pd…

Lyapunov-Based Deep Learning Control for Robots with Unknown Jacobian
Deep learning, with its exceptional learning capabilities and flexibility, has been widely applied in various applications. However, its black-box nature poses a significant challenge in real-time robotic applications, particularly in robot control, where trustworthiness and robustness are critical in ensuring safety. In robot motion control, it is essential to analyze and ensure system stability, necessitating the establishment of methodologies that address this need. This paper aims to develo…

Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights
Marzieh Ajirak, Anand Ravishankar, Petar M. Djuric
https://arxiv.org/abs/2509.05877

Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights
Uncertainty Quantification (UQ) is essential in probabilistic machine learning models, particularly for assessing the reliability of predictions. In this paper, we present a systematic framework for estimating both epistemic and aleatoric uncertainty in probabilistic models. We focus on Gaussian Process Latent Variable Models and employ scalable Random Fourier Features-based Gaussian Processes to approximate predictive distributions efficiently. We derive a theoretical formulation for UQ, propo…

RobQFL: Robust Quantum Federated Learning in Adversarial Environment
Walid El Maouaki, Nouhaila Innan, Alberto Marchisio, Taoufik Said, Muhammad Shafique, Mohamed Bennai
https://arxiv.org/abs/2509.04914

RobQFL: Robust Quantum Federated Learning in Adversarial Environment
Quantum Federated Learning (QFL) merges privacy-preserving federation with quantum computing gains, yet its resilience to adversarial noise is unknown. We first show that QFL is as fragile as centralized quantum learning. We propose Robust Quantum Federated Learning (RobQFL), embedding adversarial training directly into the federated loop. RobQFL exposes tunable axes: client coverage $γ$ (0-100\%), perturbation scheduling (fixed-$\varepsilon$ vs $\varepsilon$-mixes), and optimization (fine-tun…

Joint Communication Scheduling and Velocity Control for Multi-UAV-Assisted Post-Disaster Monitoring: An Attention-Based In-Context Learning Approach
Yousef Emami, Seyedsina Nabavirazavi, Jingjing Zheng, Hao Zhou, Miguel Gutierrez Gaitan, Kai Li, Luis Almeida
https://arxiv.org/abs/2510.05698

Joint Communication Scheduling and Velocity Control for Multi-UAV-Assisted Post-Disaster Monitoring: An Attention-Based In-Context Learning Approach
Recently, Unmanned Aerial Vehicles (UAVs) are increasingly being investigated to collect sensory data in post-disaster monitoring scenarios, such as tsunamis, where early actions are critical to limit coastal damage. A major challenge is to design the data collection schedules and flight velocities, as unfavorable schedules and velocities can lead to transmission errors and buffer overflows of the ground sensors, ultimately resulting in significant packet loss. Meanwhile, online Deep Reinforcem…

ACE-RL: Adaptive Constraint-Enhanced Reward for Long-form Generation Reinforcement Learning
Jianghao Chen, Wei Sun, Qixiang Yin, Lingxing Kong, Zhixing Tan, Jiajun Zhang
https://arxiv.org/abs/2509.04903

ACE-RL: Adaptive Constraint-Enhanced Reward for Long-form Generation Reinforcement Learning
Large Language Models (LLMs) have demonstrated remarkable progress in long-context understanding, yet they face significant challenges in high-quality long-form generation. Existing studies primarily suffer from two limitations: (1) A heavy reliance on scarce, high-quality long-form response data for supervised fine-tuning (SFT) or for pairwise preference reward in reinforcement learning (RL). (2) Focus on coarse-grained quality optimization dimensions, such as relevance, coherence, and helpful…

Adaptive Reinforcement Learning for Dynamic Configuration Allocation in Pre-Production Testing
Yu Zhu
https://arxiv.org/abs/2510.05147 https://arxiv.org/pd…

Adaptive Reinforcement Learning for Dynamic Configuration Allocation in Pre-Production Testing
Ensuring reliability in modern software systems requires rigorous pre-production testing across highly heterogeneous and evolving environments. Because exhaustive evaluation is infeasible, practitioners must decide how to allocate limited testing resources across configurations where failure probabilities may drift over time. Existing combinatorial optimization approaches are static, ad hoc, and poorly suited to such non-stationary settings. We introduce a novel reinforcement learning (RL) fram…

When Secure Isn't: Assessing the Security of Machine Learning Model Sharing
Gabriele Digregorio, Marco Di Gennaro, Stefano Zanero, Stefano Longari, Michele Carminati
https://arxiv.org/abs/2509.06703

When Secure Isn't: Assessing the Security of Machine Learning Model Sharing
The rise of model-sharing through frameworks and dedicated hubs makes Machine Learning significantly more accessible. Despite their benefits, these tools expose users to underexplored security risks, while security awareness remains limited among both practitioners and developers. To enable a more security-conscious culture in Machine Learning model sharing, in this paper we evaluate the security posture of frameworks and hubs, assess whether security-oriented mechanisms offer real protection, …

Shift Before You Learn: Enabling Low-Rank Representations in Reinforcement Learning
Bastien Dubail, Stefan Stojanovic, Alexandre Prouti\`ere
https://arxiv.org/abs/2509.05193 htt…

Shift Before You Learn: Enabling Low-Rank Representations in Reinforcement Learning
Low-rank structure is a common implicit assumption in many modern reinforcement learning (RL) algorithms. For instance, reward-free and goal-conditioned RL methods often presume that the successor measure admits a low-rank representation. In this work, we challenge this assumption by first remarking that the successor measure itself is not low-rank. Instead, we demonstrate that a low-rank structure naturally emerges in the shifted successor measure, which captures the system dynamics after bypa…

Brain Tumor Detection Through Diverse CNN Architectures in IoT Healthcare Industries: Fast R-CNN, U-Net, Transfer Learning-Based CNN, and Fully Connected CNN
Mohsen Asghari Ilani, Yaser M. Banad
https://arxiv.org/abs/2509.05821

Brain Tumor Detection Through Diverse CNN Architectures in IoT Healthcare Industries: Fast R-CNN, U-Net, Transfer Learning-Based CNN, and Fully Connected CNN
Artificial intelligence (AI)-powered deep learning has advanced brain tumor diagnosis in Internet of Things (IoT)-healthcare systems, achieving high accuracy with large datasets. Brain health is critical to human life, and accurate diagnosis is essential for effective treatment. Magnetic Resonance Imaging (MRI) provides key data for brain tumor detection, serving as a major source of big data for AI-driven image classification. In this study, we classified glioma, meningioma, and pituitary tumo…

Exploring Student Choice and the Use of Multimodal Generative AI in Programming Learning
Xinying Hou, Ruiwei Xiao, Runlong Ye, Michael Liut, John Stamper
https://arxiv.org/abs/2510.05417

Exploring Student Choice and the Use of Multimodal Generative AI in Programming Learning
The broad adoption of Generative AI (GenAI) is impacting Computer Science education, and recent studies found its benefits and potential concerns when students use it for programming learning. However, most existing explorations focus on GenAI tools that primarily support text-to-text interaction. With recent developments, GenAI applications have begun supporting multiple modes of communication, known as multimodality. In this work, we explored how undergraduate programming novices choose and w…

The impact of gamification on learning outcomes: experiences from a Biomedical Engineering course
Gonzalo R. R\'ios-Mu\~noz, Caterina Fuster-Barcelo, Arrate Mu\~noz-Barrutia
https://arxiv.org/abs/2509.06126

The impact of gamification on learning outcomes: experiences from a Biomedical Engineering course
This study examines the integration of digital tools in project-based learning within a Biomedical Engineering course to enhance collaboration, transparency, and assessment fairness. Building on prior pilot experiences, we implemented a structured learning environment that combined experiment tracking, real-time collaboration, and peer-assessment practices. The intervention was deployed across two consecutive academic years, involving master's-level students in Biomedical Image Processing. Data…

A deep multiple instance learning approach based on coarse labels for high-resolution land-cover mapping
Gianmarco Perantoni, Lorenzo Bruzzone
https://arxiv.org/abs/2510.06769 h…

A deep multiple instance learning approach based on coarse labels for high-resolution land-cover mapping
The quantity and the quality of the training labels are central problems in high-resolution land-cover mapping with machine-learning-based solutions. In this context, weak labels can be gathered in large quantities by leveraging on existing low-resolution or obsolete products. In this paper, we address the problem of training land-cover classifiers using high-resolution imagery (e.g., Sentinel-2) and weak low-resolution reference data (e.g., MODIS -derived land-cover maps). Inspired by recent w…

Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristics
Ryoga Oishi, Sho Sakaino, Toshiaki Tsuji
https://arxiv.org/abs/2509.04737 https://…

Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristics
In the field of robot learning, coordinating robot actions through language instructions is becoming increasingly feasible. However, adapting actions to human instructions remains challenging, as such instructions are often qualitative and require exploring behaviors that satisfy varying conditions. This paper proposes a motion generation model that adapts robot actions in response to modifier directives human instructions imposing behavioral conditions during task execution. The proposed metho…

Behind the scenes of the Quantum Extreme Learning Machines
A. De Lorenzis, M. P. Casado, N. Lo Gullo, T. Lux, F. Plastina, A. Riera
https://arxiv.org/abs/2509.06873 https://

Behind the scenes of the Quantum Extreme Learning Machines
In recent years, Quantum Machine Learning (QML) has grown rapidly, emerging as a promising approach to make quantum computing implementation competitive. In this work, we investigate Quantum Extreme Learning Machines (QELM), a quantum variant of Extreme Learning Machines where training is restricted to the output layer. The proposed architecture combines dimensionality reduction (via PCA or Autoencoders), quantum state encoding, evolution under an XX Hamiltonian, and measurement, providing feat…

Learning from one graph: transductive learning guarantees via the geometry of small random worlds
Nils Detering, Luca Galimberti, Anastasis Kratsios, Giulia Livieri, A. Martina Neuman
https://arxiv.org/abs/2509.06894

Learning from one graph: transductive learning guarantees via the geometry of small random worlds
Since their introduction by Kipf and Welling in $2017$, a primary use of graph convolutional networks is transductive node classification, where missing labels are inferred within a single observed graph and its feature matrix. Despite the widespread use of the network model, the statistical foundations of transductive learning remain limited, as standard inference frameworks typically rely on multiple independent samples rather than a single graph. In this work, we address these gaps by develo…

Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning
Yuxuan Bai, Gauri Pradhan, Marlon Tobaben, Antti Honkela
https://arxiv.org/abs/2510.05753 https://…

Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning
With the emergence of powerful large-scale foundation models, the training paradigm is increasingly shifting from from-scratch training to transfer learning. This enables high utility training with small, domain-specific datasets typical in sensitive applications.Membership inference attacks (MIAs) provide an empirical estimate of the privacy leakage by machine learning models. Yet, prior assessments of MIAs against models fine-tuned with transfer learning rely on a small subset of possible att…

Optimizing Small Transformer-Based Language Models for Multi-Label Sentiment Analysis in Short Texts
Julius Neumann, Robert Lange, Yuni Susanti, Michael F\"arber
https://arxiv.org/abs/2509.04982

Optimizing Small Transformer-Based Language Models for Multi-Label Sentiment Analysis in Short Texts
Sentiment classification in short text datasets faces significant challenges such as class imbalance, limited training samples, and the inherent subjectivity of sentiment labels -- issues that are further intensified by the limited context in short texts. These factors make it difficult to resolve ambiguity and exacerbate data sparsity, hindering effective learning. In this paper, we evaluate the effectiveness of small Transformer-based models (i.e., BERT and RoBERTa, with fewer than 1 billion …

TalkToAgent: A Human-centric Explanation of Reinforcement Learning Agents with Large Language Models
Haechang Kim, Hao Chen, Can Li, Jong Min Lee
https://arxiv.org/abs/2509.04809

TalkToAgent: A Human-centric Explanation of Reinforcement Learning Agents with Large Language Models
Explainable Reinforcement Learning (XRL) has emerged as a promising approach in improving the transparency of Reinforcement Learning (RL) agents. However, there remains a gap between complex RL policies and domain experts, due to the limited comprehensibility of XRL results and isolated coverage of current XRL approaches that leave users uncertain about which tools to employ. To address these challenges, we introduce TalkToAgent, a multi-agent Large Language Models (LLM) framework that delivers…

Software Dependencies 2.0: An Empirical Study of Reuse and Integration of Pre-Trained Models in Open-Source Projects
Jerin Yasmin, Wenxin Jiang, James C. Davis, Yuan Tian
https://arxiv.org/abs/2509.06085

Software Dependencies 2.0: An Empirical Study of Reuse and Integration of Pre-Trained Models in Open-Source Projects
Pre-trained models (PTMs) are machine learning models that have been trained in advance, often on large-scale data, and can be reused for new tasks, thereby reducing the need for costly training from scratch. Their widespread adoption introduces a new class of software dependency, which we term Software Dependencies 2.0, extending beyond conventional libraries to learned behaviors embodied in trained models and their associated artifacts. The integration of PTMs as software dependencies in real…

Dataset Ownership in the Era of Large Language Models
Kun Li, Cheng Wang, Minghui Xu, Yue Zhang, Xiuzhen Cheng
https://arxiv.org/abs/2509.05921 https://arx…

Dataset Ownership in the Era of Large Language Models
As datasets become critical assets in modern machine learning systems, ensuring robust copyright protection has emerged as an urgent challenge. Traditional legal mechanisms often fail to address the technical complexities of digital data replication and unauthorized use, particularly in opaque or decentralized environments. This survey provides a comprehensive review of technical approaches for dataset copyright protection, systematically categorizing them into three main classes: non-intrusive…

Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI
Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel, Ahsan Habib Tareq, Iftekhar Haider
https://arxiv.org/abs/2510.06259

Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI
Medical AI faces challenges in privacy-preserving collaborative learning while ensuring fairness across heterogeneous healthcare institutions. Current federated learning approaches suffer from static architectures, slow convergence (45-73 rounds), fairness gaps marginalizing smaller institutions, and scalability constraints (15-client limit). We propose Adaptive Fair Federated Learning (AFFL) through three innovations: (1) Adaptive Knowledge Messengers dynamically scaling capacity based on hete…

A biologically inspired separable learning vision model for real-time traffic object perception in Dark
Hulin Li, Qiliang Ren, Jun Li, Hanbing Wei, Zheng Liu, Linfang Fan
https://arxiv.org/abs/2509.05012

A biologically inspired separable learning vision model for real-time traffic object perception in Dark
Fast and accurate object perception in low-light traffic scenes has attracted increasing attention. However, due to severe illumination degradation and the lack of reliable visual cues, existing perception models and methods struggle to quickly adapt to and accurately predict in low-light environments. Moreover, there is the absence of available large-scale benchmark specifically focused on low-light traffic scenes. To bridge this gap, we introduce a physically grounded illumination degradation…

In-Context Policy Adaptation via Cross-Domain Skill Diffusion
Minjong Yoo, Woo Kyung Kim, Honguk Woo
https://arxiv.org/abs/2509.04535 https://arxiv.org/pdf…

In-Context Policy Adaptation via Cross-Domain Skill Diffusion
In this work, we present an in-context policy adaptation (ICPAD) framework designed for long-horizon multi-task environments, exploring diffusion-based skill learning techniques in cross-domain settings. The framework enables rapid adaptation of skill-based reinforcement learning policies to diverse target domains, especially under stringent constraints on no model updates and only limited target domain data. Specifically, the framework employs a cross-domain skill diffusion scheme, where domai…

Communication Enables Cooperation in LLM Agents: A Comparison with Curriculum-Based Approaches
Hachem Madmoun, Salem Lahlou
https://arxiv.org/abs/2510.05748 https://

Communication Enables Cooperation in LLM Agents: A Comparison with Curriculum-Based Approaches
Eliciting cooperation in multi-agent LLM systems is critical for AI alignment. We investigate two approaches: direct communication and curriculum learning. In a 4-player Stag Hunt, a one-word "cheap talk" channel increases cooperation from 0% to 48.3%, demonstrating communication as a robust coordination mechanism. In contrast, we find that curriculum learning is highly sensitive to design choices: our pedagogical curriculum through progressively complex games reduced agent payoffs by 27.4% in …

Potential of multi-anomalies detection using quantum machine learning
Takao Tomono, Kazuya Tsujimura
https://arxiv.org/abs/2510.07055 https://arxiv.org/pdf…

Potential of multi-anomalies detection using quantum machine learning
Maintenance of production equipment is critical in manufacturing. Typically, machine learning models are trained on sensor data closely attached to equipment. However, as the number of machines increases, computational cost grows rapidly. In practice, anomalies are often identified by human operators through auditory perception, relying heavily on experience and intuition. In vibration analysis, especially, AR model coefficients combined with one-class SVMs are used for detecting anomalies. In …

Learning to Rewrite Prompts for Bootstrapping LLMs on Downstream Tasks
Qinhao Zhou, Xiang Xiang, Kun He, John E. Hopcroft
https://arxiv.org/abs/2510.06695 https://

Learning to Rewrite Prompts for Bootstrapping LLMs on Downstream Tasks
In recent years, the growing interest in Large Language Models (LLMs) has significantly advanced prompt engineering, transitioning from manual design to model-based optimization. Prompts for LLMs generally comprise two components: the \textit{instruction}, which defines the task or objective, and the \textit{input}, which is tailored to the instruction type. In natural language generation (NLG) tasks such as machine translation, the \textit{input} component is particularly critical, while the \…

Implicit Updates for Average-Reward Temporal Difference Learning
Hwanwoo Kim, Dongkyu Derek Cho, Eric Laber
https://arxiv.org/abs/2510.06149 https://arxiv.…

Implicit Updates for Average-Reward Temporal Difference Learning
Temporal difference (TD) learning is a cornerstone of reinforcement learning. In the average-reward setting, standard TD($λ$) is highly sensitive to the choice of step-size and thus requires careful tuning to maintain numerical stability. We introduce average-reward implicit TD($λ$), which employs an implicit fixed point update to provide data-adaptive stabilization while preserving the per iteration computational complexity of standard average-reward TD($λ$). In contrast to prior finite-tim…

Language-Driven Hierarchical Task Structures as Explicit World Models for Multi-Agent Learning
Brennen Hill
https://arxiv.org/abs/2509.04731 https://arxiv.…

Language-Driven Hierarchical Task Structures as Explicit World Models for Multi-Agent Learning
The convergence of Language models, Agent models, and World models represents a critical frontier for artificial intelligence. While recent progress has focused on scaling Language and Agent models, the development of sophisticated, explicit World Models remains a key bottleneck, particularly for complex, long-horizon multi-agent tasks. In domains such as robotic soccer, agents trained via standard reinforcement learning in high-fidelity but structurally-flat simulators often fail due to intrac…

QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification
Arpit Kapoor, Rohitash Chandra
https://arxiv.org/abs/2510.05453

QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification
Conceptual rainfall-runoff models aid hydrologists and climate scientists in modelling streamflow to inform water management practices. Recent advances in deep learning have unravelled the potential for combining hydrological models with deep learning models for better interpretability and improved predictive performance. In our previous work, we introduced DeepGR4J, which enhanced the GR4J conceptual rainfall-runoff model using a deep learning model to serve as a surrogate for the routing comp…

Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
Soroosh Tayebi Arasteh, Mina Shaigan, Christiane Kuhl, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn
https://arxiv.org/abs/2510.07191

Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
Self-supervised learning (SSL) has advanced visual representation learning, but its value in chest radiography, a high-volume imaging modality with fine-grained findings, remains unclear. Meta's DINOv3 extends earlier SSL models through Gram-anchored self-distillation. Whether these design choices improve transfer learning for chest radiography has not been systematically tested. We benchmarked DINOv3 against DINOv2 and ImageNet initialization across seven datasets (n>814,000). Two representati…

ZLATTE: A Geometry-Aware, Learning-Free Framework for Language-Driven Trajectory Reshaping in Human-Robot Interaction
Junhui Huang, Yuhe Gong, Changsheng Li, Xingguang Duan, Luis Figueredo
https://arxiv.org/abs/2509.06031

ZLATTE: A Geometry-Aware, Learning-Free Framework for Language-Driven Trajectory Reshaping in Human-Robot Interaction
We present ZLATTE, a geometry-aware, learning-free framework for language-driven trajectory reshaping in human-robot interaction. Unlike prior learning-based methods, ZLATTE leverages Vision-Language Models to register objects as geometric primitives and employs a Large Language Model to translate natural language instructions into explicit geometric and kinematic constraints. These constraints are integrated into a potential field optimization to adapt initial trajectories while preserving fea…

Bionetta: Efficient Client-Side Zero-Knowledge Machine Learning Proving
Dmytro Zakharov, Oleksandr Kurbatov, Artem Sdobnov, Lev Soukhanov, Yevhenii Sekhin, Vitalii Volovyk, Mykhailo Velykodnyi, Mark Cherepovskyi, Kyrylo Baibula, Lasha Antadze, Pavlo Kravchenko, Volodymyr Dubinin, Yaroslav Panasenko
https://arxiv.org/abs/2510.06784

Bionetta: Efficient Client-Side Zero-Knowledge Machine Learning Proving
In this report, we compare the performance of our UltraGroth-based zero-knowledge machine learning framework Bionetta to other tools of similar purpose such as EZKL, Lagrange's deep-prove, or zkml. The results show a significant boost in the proving time for custom-crafted neural networks: they can be proven even on mobile devices, enabling numerous client-side proving applications. While our scheme increases the cost of one-time preprocessing steps, such as circuit compilation and generating t…

UNH at CheckThat! 2025: Fine-tuning Vs Prompting in Claim Extraction
Joe Wilder, Nikhil Kadapala, Benji Xu, Mohammed Alsaadi, Aiden Parsons, Mitchell Rogers, Palash Agarwal, Adam Hassick, Laura Dietz
https://arxiv.org/abs/2509.06883

UNH at CheckThat! 2025: Fine-tuning Vs Prompting in Claim Extraction
We participate in CheckThat! Task 2 English and explore various methods of prompting and in-context learning, including few-shot prompting and fine-tuning with different LLM families, with the goal of extracting check-worthy claims from social media passages. Our best METEOR score is achieved by fine-tuning a FLAN-T5 model. However, we observe that higher-quality claims can sometimes be extracted using other methods, even when their METEOR scores are lower.

On the Theory of Continual Learning with Gradient Descent for Neural Networks
Hossein Taheri, Avishek Ghosh, Arya Mazumdar
https://arxiv.org/abs/2510.05573 https://

On the Theory of Continual Learning with Gradient Descent for Neural Networks
Continual learning, the ability of a model to adapt to an ongoing sequence of tasks without forgetting the earlier ones, is a central goal of artificial intelligence. To shed light on its underlying mechanisms, we analyze the limitations of continual learning in a tractable yet representative setting. In particular, we study one-hidden-layer quadratic neural networks trained by gradient descent on an XOR cluster dataset with Gaussian noise, where different tasks correspond to different clusters…

Uncertainty in Machine Learning
Hans Weytjens, Wouter Verbeke
https://arxiv.org/abs/2510.06007 https://arxiv.org/pdf/2510.06007

Uncertainty in Machine Learning
This book chapter introduces the principles and practical applications of uncertainty quantification in machine learning. It explains how to identify and distinguish between different types of uncertainty and presents methods for quantifying uncertainty in predictive models, including linear regression, random forests, and neural networks. The chapter also covers conformal prediction as a framework for generating predictions with predefined confidence intervals. Finally, it explores how uncerta…

Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study
Mohammad Abbadi, Yassine Himeur, Shadi Atalla, Wathiq Mansoor
https://arxiv.org/abs/2509.05004

Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study
Breast cancer remains a leading cause of cancer-related mortality among women worldwide. Ultrasound imaging, widely used due to its safety and cost-effectiveness, plays a key role in early detection, especially in patients with dense breast tissue. This paper presents a comprehensive study on the application of machine learning and deep learning techniques for breast cancer classification using ultrasound images. Using datasets such as BUSI, BUS-BRA, and BrEaST-Lesions USG, we evaluate classica…

Learning to Crawl: Latent Model-Based Reinforcement Learning for Soft Robotic Adaptive Locomotion
Vaughn Gzenda, Robin Chhabra
https://arxiv.org/abs/2510.05957 https://

Learning to Crawl: Latent Model-Based Reinforcement Learning for Soft Robotic Adaptive Locomotion
Soft robotic crawlers are mobile robots that utilize soft body deformability and compliance to achieve locomotion through surface contact. Designing control strategies for such systems is challenging due to model inaccuracies, sensor noise, and the need to discover locomotor gaits. In this work, we present a model-based reinforcement learning (MB-RL) framework in which latent dynamics inferred from onboard sensors serve as a predictive model that guides an actor-critic algorithm to optimize loc…

In-the-Flow Agentic System Optimization for Effective Planning and Tool Use
Zhuofeng Li, Haoxiang Zhang, Seungju Han, Sheng Liu, Jianwen Xie, Yu Zhang, Yejin Choi, James Zou, Pan Lu
https://arxiv.org/abs/2510.05592

In-the-Flow Agentic System Optimization for Effective Planning and Tool Use
Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios. Agentic systems offer a promising alternative by decomposing work across specialized modules, yet most remain training-free or rely on offline training decoupled from the l…

Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest
Xiao Yang, Mehdi Ben Ayed, Longyu Zhao, Fan Zhou, Yuchen Shen, Abe Engle, Jinfeng Zhuang, Ling Leng, Jiajing Xu, Charles Rosenberg, Prathibha Deshikachar
https://arxiv.org/abs/2509.05292

Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest
The ranking utility function in an ad recommender system, which linearly combines predictions of various business goals, plays a central role in balancing values across the platform, advertisers, and users. Traditional manual tuning, while offering simplicity and interpretability, often yields suboptimal results due to its unprincipled tuning objectives, the vast amount of parameter combinations, and its lack of personalization and adaptability to seasonality. In this work, we propose a general…

Pothole Detection and Recognition based on Transfer Learning
Mang Hu, Qianqian Xia
https://arxiv.org/abs/2509.06750 https://arxiv.org/pdf/2509.06750…

Pothole Detection and Recognition based on Transfer Learning
With the rapid development of computer vision and machine learning, automated methods for pothole detection and recognition based on image and video data have received significant attention. It is of great significance for social development to conduct an in-depth analysis of road images through feature extraction, thereby achieving automatic identification of the pothole condition in new images. Consequently, this is the main issue addressed in this study. Based on preprocessing techniques suc…

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation
Tien Pham, Xinyun Chi, Khang Nguyen, Manfred Huber, Angelo Cangelosi
https://arxiv.org/abs/2509.04970

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation
Reinforcement learning (RL) agents can learn to solve complex tasks from visual inputs, but generalizing these learned skills to new environments remains a major challenge in RL application, especially robotics. While data augmentation can improve generalization, it often compromises sample efficiency and training stability. This paper introduces DeGuV, an RL framework that enhances both generalization and sample efficiency. In specific, we leverage a learnable masker network that produces a ma…

Analyzing Finnish Inflectional Classes through Discriminative Lexicon and Deep Learning Models
Alexandre Nikolaev, Yu-Ying Chuang, R. Harald Baayen
https://arxiv.org/abs/2509.04813

Analyzing Finnish Inflectional Classes through Discriminative Lexicon and Deep Learning Models
Descriptions of complex nominal or verbal systems make use of inflectional classes. Inflectional classes bring together nouns which have similar stem changes and use similar exponents in their paradigms. Although inflectional classes can be very useful for language teaching as well as for setting up finite state morphological systems, it is unclear whether inflectional classes are cognitively real, in the sense that native speakers would need to discover these classes in order to learn how to p…

DecompGAIL: Learning Realistic Traffic Behaviors with Decomposed Multi-Agent Generative Adversarial Imitation Learning
Ke Guo, Haochen Liu, Xiaojun Wu, Chen Lv
https://arxiv.org/abs/2510.06913

DecompGAIL: Learning Realistic Traffic Behaviors with Decomposed Multi-Agent Generative Adversarial Imitation Learning
Realistic traffic simulation is critical for the development of autonomous driving systems and urban mobility planning, yet existing imitation learning approaches often fail to model realistic traffic behaviors. Behavior cloning suffers from covariate shift, while Generative Adversarial Imitation Learning (GAIL) is notoriously unstable in multi-agent settings. We identify a key source of this instability: irrelevant interaction misguidance, where a discriminator penalizes an ego vehicle's reali…

Revisiting Node Affinity Prediction in Temporal Graphs
Krishna Sri Ipsit Mantri, Or Feldman, Moshe Eliasof, Chaim Baskin
https://arxiv.org/abs/2510.06940 https://

Revisiting Node Affinity Prediction in Temporal Graphs
Node affinity prediction is a common task that is widely used in temporal graph learning with applications in social and financial networks, recommender systems, and more. Recent works have addressed this task by adapting state-of-the-art dynamic link property prediction models to node affinity prediction. However, simple heuristics, such as Persistent Forecast or Moving Average, outperform these models. In this work, we analyze the challenges in training current Temporal Graph Neural Networks …

Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)
Yifei Ren, Edward Johns
https://arxiv.org/abs/2509.06191 https://

Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)
Recent 3D generative models, which are capable of generating full object shapes from just a few images, now open up new opportunities in robotics. In this work, we show that 3D generative models can be used to augment a dataset from a single real-world demonstration, after which an omnidirectional policy can be learned within this imagined dataset. We found that this enables a robot to perform a task when initialised from states very far from those observed during the demonstration, including s…

TTRV: Test-Time Reinforcement Learning for Vision Language Models
Akshit Singh, Shyam Marjit, Wei Lin, Paul Gavrikov, Serena Yeung-Levy, Hilde Kuehne, Rogerio Feris, Sivan Doveh, James Glass, M. Jehanzeb Mirza
https://arxiv.org/abs/2510.06783

TTRV: Test-Time Reinforcement Learning for Vision Language Models
Existing methods for extracting reward signals in Reinforcement Learning typically rely on labeled data and dedicated training splits, a setup that contrasts with how humans learn directly from their environment. In this work, we propose TTRV to enhance vision language understanding by adapting the model on the fly at inference time, without the need for any labeled data. Concretely, we enhance the Group Relative Policy Optimization (GRPO) framework by designing rewards based on the frequency o…

A transformer-BiGRU-based framework with data augmentation and confident learning for network intrusion detection
Jiale Zhang, Pengfei He, Fei Li, Kewei Li, Yan Wang, Lan Huang, Ruochi Zhang, Fengfeng Zhou
https://arxiv.org/abs/2509.04925

A transformer-BiGRU-based framework with data augmentation and confident learning for network intrusion detection
In today's fast-paced digital communication, the surge in network traffic data and frequency demands robust and precise network intrusion solutions. Conventional machine learning methods struggle to grapple with complex patterns within the vast network intrusion datasets, which suffer from data scarcity and class imbalance. As a result, we have integrated machine learning and deep learning techniques within the network intrusion detection system to bridge this gap. This study has developed Trai…

Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?
Jan Fiszer, Dominika Ciupek, Maciej Malawski
https://arxiv.org/abs/2510.07126

Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?
Deep learning (DL) has been increasingly applied in medical imaging, however, it requires large amounts of data, which raises many challenges related to data privacy, storage, and transfer. Federated learning (FL) is a training paradigm that overcomes these issues, though its effectiveness may be reduced when dealing with non-independent and identically distributed (non-IID) data. This study simulates non-IID conditions by applying different MRI intensity normalization techniques to separate da…

Discriminative Feature Feedback with General Teacher Classes
Omri Bar Oz, Tosca Lechner, Sivan Sabato
https://arxiv.org/abs/2510.07245 https://arxiv.org/pd…

Discriminative Feature Feedback with General Teacher Classes
We study the theoretical properties of the interactive learning protocol Discriminative Feature Feedback (DFF) (Dasgupta et al., 2018). The DFF learning protocol uses feedback in the form of discriminative feature explanations. We provide the first systematic study of DFF in a general framework that is comparable to that of classical protocols such as supervised learning and online learning. We study the optimal mistake bound of DFF in the realizable and the non-realizable settings, and obtain …

Learning to Walk in Costume: Adversarial Motion Priors for Aesthetically Constrained Humanoids
Arturo Flores Alvarez, Fatemeh Zargarbashi, Havel Liu, Shiqi Wang, Liam Edwards, Jessica Anz, Alex Xu, Fan Shi, Stelian Coros, Dennis W. Hong
https://arxiv.org/abs/2509.05581

Learning to Walk in Costume: Adversarial Motion Priors for Aesthetically Constrained Humanoids
We present a Reinforcement Learning (RL)-based locomotion system for Cosmo, a custom-built humanoid robot designed for entertainment applications. Unlike traditional humanoids, entertainment robots present unique challenges due to aesthetic-driven design choices. Cosmo embodies these with a disproportionately large head (16% of total mass), limited sensing, and protective shells that considerably restrict movement. To address these challenges, we apply Adversarial Motion Priors (AMP) to enable …

VIM-GS: Visual-Inertial Monocular Gaussian Splatting via Object-level Guidance in Large Scenes
Shengkai Zhang, Yuhe Liu, Guanjun Wu, Jianhua He, Xinggang Wang, Mozi Chen, Kezhong Liu
https://arxiv.org/abs/2509.06685

VIM-GS: Visual-Inertial Monocular Gaussian Splatting via Object-level Guidance in Large Scenes
VIM-GS is a Gaussian Splatting (GS) framework using monocular images for novel-view synthesis (NVS) in large scenes. GS typically requires accurate depth to initiate Gaussian ellipsoids using RGB-D/stereo cameras. Their limited depth sensing range makes it difficult for GS to work in large scenes. Monocular images, however, lack depth to guide the learning and lead to inferior NVS results. Although large foundation models (LFMs) for monocular depth estimation are available, they suffer from cro…

Physics-Informed Machine Learning in Biomedical Science and Engineering
Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey, George Em Karniadakis
https://arxiv.org/abs/2510.05433 htt…

Physics-Informed Machine Learning in Biomedical Science and Engineering
Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations…

Toward Accessible Dermatology: Skin Lesion Classification Using Deep Learning Models on Mobile-Acquired Images
Asif Newaz, Masum Mushfiq Ishti, A Z M Ashraful Azam, Asif Ur Rahman Adib
https://arxiv.org/abs/2509.04800

Toward Accessible Dermatology: Skin Lesion Classification Using Deep Learning Models on Mobile-Acquired Images
Skin diseases are among the most prevalent health concerns worldwide, yet conventional diagnostic methods are often costly, complex, and unavailable in low-resource settings. Automated classification using deep learning has emerged as a promising alternative, but existing studies are mostly limited to dermoscopic datasets and a narrow range of disease classes. In this work, we curate a large dataset of over 50 skin disease categories captured with mobile devices, making it more representative o…

Deep Learning-Enhanced for Amine Emission Monitoring and Performance Analysis in Industrial Carbon Capture Plants
Lokendra Poudel, David Tincher, Duy-Nhat Phan, Rahul Bhowmik
https://arxiv.org/abs/2509.05241

Deep Learning-Enhanced for Amine Emission Monitoring and Performance Analysis in Industrial Carbon Capture Plants
We present data driven deep learning models for forecasting and monitoring amine emissions and key performance parameters in amine-based post-combustion carbon capture systems. Using operational data from the CESAR1 solvent campaign at Technology Center Mongstad, four DL architectures such as Basic Long Short-Term Memory (LSTM), Stacked LSTM, Bi-directional LSTM, and Convolutional LSTM were developed to capture time-dependent process behavior. For emission prediction, models were designed for 2…

Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization
Kanglei Zhou, Qingyi Pan, Xingxing Zhang, Hubert P. H. Shum, Frederick W. B. Li, Xiaohui Liang, Liyuan Wang
https://arxiv.org/abs/2510.06842

Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization
Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation. A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios, which limits the generalization ability of conventional methods. We introduce Continual AQA (CAQA), which equips AQA with Continual Learning (CL) capabilities to handle evolving distributions while mitigating catastrophic forgetting. Although param…

Detecting Blinks in Healthy and Parkinson's EEG: A Deep Learning Perspective
Artem Lensky, Yiding Qiu
https://arxiv.org/abs/2509.04951 https://arxiv.or…

Detecting Blinks in Healthy and Parkinson's EEG: A Deep Learning Perspective
Blinks in electroencephalography (EEG) are often treated as unwanted artifacts. However, recent studies have demonstrated that blink rate and its variability are important physiological markers to monitor cognitive load, attention, and potential neurological disorders. This paper addresses the critical task of accurate blink detection by evaluating various deep learning models for segmenting EEG signals into involuntary blinks and non-blinks. We present a pipeline for blink detection using 1, 3…

Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networks
Yuxi Wang, Heyao Liu, Guanzi Yao, Nyutian Long, Yue Kang
https://arxiv.org/abs/2509.04973 https…

Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networks
This paper proposes a topology-aware graph reinforcement learning approach to address the routing policy optimization problem in cloud server environments. The method builds a unified framework for state representation and structural evolution by integrating a Structure-Aware State Encoding (SASE) module and a Policy-Adaptive Graph Update (PAGU) mechanism. It aims to tackle the challenges of decision instability and insufficient structural awareness under dynamic topologies. The SASE module mod…

Self-supervised Physics-guided Model with Implicit Representation Regularization for Fast MRI Reconstruction
Jingran Xu, Yuanyuan Liu, Yanjie Zhu
https://arxiv.org/abs/2510.06611

Self-supervised Physics-guided Model with Implicit Representation Regularization for Fast MRI Reconstruction
Magnetic Resonance Imaging (MRI) is a vital clinical diagnostic tool, yet its widespread application is limited by prolonged scan times. Fast MRI reconstruction techniques effectively reduce acquisition duration by reconstructing high-fidelity MR images from undersampled k-space data. In recent years, deep learning-based methods have demonstrated remarkable progress in this field, with self-supervised and unsupervised learning approaches proving particularly valuable in scenarios where fully sa…

Stratified GRPO: Handling Structural Heterogeneity in Reinforcement Learning of LLM Search Agents
Mingkang Zhu, Xi Chen, Bei Yu, Hengshuang Zhao, Jiaya Jia
https://arxiv.org/abs/2510.06214

Stratified GRPO: Handling Structural Heterogeneity in Reinforcement Learning of LLM Search Agents
Large language model (LLM) agents increasingly rely on external tools such as search engines to solve complex, multi-step problems, and reinforcement learning (RL) has become a key paradigm for training them. However, the trajectories of search agents are structurally heterogeneous, where variations in the number, placement, and outcomes of search calls lead to fundamentally different answer directions and reward distributions. Standard policy gradient methods, which use a single global baselin…

Riddled basin geometry sets fundamental limits to predictability and reproducibility in deep learning
Andrew Ly, Pulin Gong
https://arxiv.org/abs/2510.05606 https://

Riddled basin geometry sets fundamental limits to predictability and reproducibility in deep learning
Fundamental limits to predictability are central to our understanding of many physical and computational systems. Here we show that, despite its remarkable capabilities, deep learning exhibits such fundamental limits rooted in the fractal, riddled geometry of its basins of attraction: any initialization that leads to one solution lies arbitrarily close to another that leads to a different one. We derive sufficient conditions for the emergence of riddled basins by analytically linking features w…

Greener Deep Reinforcement Learning: Analysis of Energy and Carbon Efficiency Across Atari Benchmarks
Jason Gardner, Ayan Dutta, Swapnoneel Roy, O. Patrick Kreidl, Ladislau Boloni
https://arxiv.org/abs/2509.05273

Greener Deep Reinforcement Learning: Analysis of Energy and Carbon Efficiency Across Atari Benchmarks
The growing computational demands of deep reinforcement learning (DRL) have raised concerns about the environmental and economic costs of training large-scale models. While algorithmic efficiency in terms of learning performance has been extensively studied, the energy requirements, greenhouse gas emissions, and monetary costs of DRL algorithms remain largely unexplored. In this work, we present a systematic benchmarking study of the energy consumption of seven state-of-the-art DRL algorithms, …

Transfer Learning on Edge Connecting Probability Estimation under Graphon Model
Yuyao Wang, Yu-Hung Cheng, Debarghya Mukherjee, Huimin Cheng
https://arxiv.org/abs/2510.05527 htt…

Transfer Learning on Edge Connecting Probability Estimation under Graphon Model
Graphon models provide a flexible nonparametric framework for estimating latent connectivity probabilities in networks, enabling a range of downstream applications such as link prediction and data augmentation. However, accurate graphon estimation typically requires a large graph, whereas in practice, one often only observes a small-sized network. One approach to addressing this issue is to adopt a transfer learning framework, which aims to improve estimation in a small target graph by leveragi…

Fisher Information, Training and Bias in Fourier Regression Models
Lorenzo Pastori, Veronika Eyring, Mierk Schwabe
https://arxiv.org/abs/2510.06945 https://

Fisher Information, Training and Bias in Fourier Regression Models
Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evaluation metrics based on the Fisher information matrix (FIM) are effective for predicting their training and prediction performance. We exploit the equivalence between a broad class of QNNs and Fourier models, and study the interplay between the \emph{effective dimension} and the \emph{bias} of a model towards a given task, investigating how these affe…

Learning to accelerate distributed ADMM using graph neural networks
Henri Doerks, Paul H\"ausner, Daniel Hern\'andez Escobar, Jens Sj\"olund
https://arxiv.org/abs/2509.05288

Learning to accelerate distributed ADMM using graph neural networks
Distributed optimization is fundamental in large-scale machine learning and control applications. Among existing methods, the Alternating Direction Method of Multipliers (ADMM) has gained popularity due to its strong convergence guarantees and suitability for decentralized computation. However, ADMM often suffers from slow convergence and sensitivity to hyperparameter choices. In this work, we show that distributed ADMM iterations can be naturally represented within the message-passing framewor…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/6]:
- FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki

Efficient Learning-based Graph Simulation for Temporal Graphs
Sheng Xiang, Chenhao Xu, Dawei Cheng, Xiaoyang Wang, Ying Zhang
https://arxiv.org/abs/2510.05569 https://

Efficient Learning-based Graph Simulation for Temporal Graphs
Graph simulation has recently received a surge of attention in graph processing and analytics. In real-life applications, e.g. social science, biology, and chemistry, many graphs are composed of a series of evolving graphs (i.e., temporal graphs). While most of the existing graph generators focus on static graphs, the temporal information of the graphs is ignored. In this paper, we focus on simulating temporal graphs, which aim to reproduce the structural and temporal properties of the observed…

Prior-Aligned Meta-RL: Thompson Sampling with Learned Priors and Guarantees in Finite-Horizon MDPs
Runlin Zhou, Chixiang Chen, Elynn Chen
https://arxiv.org/abs/2510.05446 https:…

Prior-Aligned Meta-RL: Thompson Sampling with Learned Priors and Guarantees in Finite-Horizon MDPs
We study meta-reinforcement learning in finite-horizon MDPs where related tasks share similar structures in their optimal action-value functions. Specifically, we posit a linear representation $Q^*_h(s,a)=Φ_h(s,a)\,θ^{(k)}_h$ and place a Gaussian meta-prior $ \mathcal{N}(θ^*_h,Σ^*_h)$ over the task-specific parameters $θ^{(k)}_h$. Building on randomized value functions, we propose two Thompson-style algorithms: (i) MTSRL, which learns only the prior mean and performs posterior sampling wit…

Generative Dynamic Graph Representation Learning for Conspiracy Spoofing Detection
Sheng Xiang, Yidong Jiang, Yunting Chen, Dawei Cheng, Guoping Zhao, Changjun Jiang
https://arxiv.org/abs/2510.05562

Generative Dynamic Graph Representation Learning for Conspiracy Spoofing Detection
Spoofing detection in financial trading is crucial, especially for identifying complex behaviors such as conspiracy spoofing. Traditional machine-learning approaches primarily focus on isolated node features, often overlooking the broader context of interconnected nodes. Graph-based techniques, particularly Graph Neural Networks (GNNs), have advanced the field by leveraging relational information effectively. However, in real-world spoofing detection datasets, trading behaviors exhibit dynamic,…

Spiral Model Technique For Data Science & Machine Learning Lifecycle
Rohith Mahadevan
https://arxiv.org/abs/2510.06987 https://arxiv.org/pdf/2510.06987…

Spiral Model Technique For Data Science & Machine Learning Lifecycle
Analytics play an important role in modern business. Companies adapt data science lifecycles to their culture to seek productivity and improve their competitiveness among others. Data science lifecycles are fairly an important contributing factor to start and end a project that are data dependent. Data science and Machine learning life cycles comprises of series of steps that are involved in a project. A typical life cycle states that it is a linear or cyclical model that revolves around. It is…

Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights
Cosmin-Andrei Hatfaludi, Alex Serban
https://arxiv.org/abs/2509.05142 https://

Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights
Federated learning has the potential to unlock siloed data and distributed resources by enabling collaborative model training without sharing private data. As more complex foundational models gain widespread use, the need to expand training resources and integrate privately owned data grows as well. In this article, we explore the intersection of federated learning and foundational models, aiming to identify, categorize, and characterize technical methods that integrate the two paradigms. As a …

Directed Evolution of Proteins via Bayesian Optimization in Embedding Space
Matou\v{s} Sold\'at, Ji\v{r}\'i Kl\'ema
https://arxiv.org/abs/2509.04998 https://

Directed Evolution of Proteins via Bayesian Optimization in Embedding Space
Directed evolution is an iterative laboratory process of designing proteins with improved function by iteratively synthesizing new protein variants and evaluating their desired property with expensive and time-consuming biochemical screening. Machine learning methods can help select informative or promising variants for screening to increase their quality and reduce the amount of necessary screening. In this paper, we present a novel method for machine-learning-assisted directed evolution of pr…

The Physics of Data and Tasks: Theories of Locality and Compositionality in Deep Learning
Alessandro Favero
https://arxiv.org/abs/2510.06106 https://arxiv.…

The Physics of Data and Tasks: Theories of Locality and Compositionality in Deep Learning
Deep neural networks have achieved remarkable success, yet our understanding of how they learn remains limited. These models can learn high-dimensional tasks, which is generally statistically intractable due to the curse of dimensionality. This apparent paradox suggests that learnable data must have an underlying latent structure. What is the nature of this structure? How do neural networks encode and exploit it, and how does it quantitatively impact performance - for instance, how does general…