Tootfinder

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Moona Kanwal, Muhammad Sami Siddiqui, Syed Anael Ali
https://arxiv.org/abs/2510.10263

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Profiling gamers provides critical insights for adaptive game design, behavioral understanding, and digital well-being. This study proposes an integrated, data-driven framework that combines psychological measures, behavioral analytics, and machine learning to reveal underlying gamer personas. A structured survey of 250 participants, including 113 active gamers, captured multidimensional behavioral, motivational, and social data. The analysis pipeline integrated feature engineering, association…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[8/8]:
- Unsupervised Active Learning via Natural Feature Progressive Framework
Yuxi Liu, Catherine Lalman, Yimin Yang

Modeling Quantum Geometry for Fractional Chern Insulators with unsupervised learning
Ang-Kun Wu, Louis Primeau, Jingtao Zhang, Kai Sun, Yang Zhang, Shi-Zeng Lin
https://arxiv.org/abs/2510.03018

Modeling Quantum Geometry for Fractional Chern Insulators with unsupervised learning
Fractional Chern insulators (FCIs) in moire materials present a unique platform for exploring strongly correlated topological phases beyond the paradigm of ideal quantum geometry. While analytical approaches to FCIs and fractional quantum Hall states (FQHS) often rely on idealized Bloch wavefunctions, realistic moire models lack direct tunability of quantum metric and Berry curvature, limiting theoretical and numerical exploration. Here, we introduce an unsupervised machine learning framework t…

Unsupervised Machine Learning for Anomaly Detection in LHC Collider Searches
Antonio D'Avanzo (on behalf of the ATLAS Collaboration)
https://arxiv.org/abs/2509.24723 https:/…

Unsupervised Machine Learning for Anomaly Detection in LHC Collider Searches
Searches for new physics at the LHC at CERN traditionally use advanced simulations to model Standard Model and new-physics processes in high-energy collisions and compare them with data. The lack of recent direct discoveries, however, has motivated the development of model-independent approaches in HEP to complement existing hypothesis-driven analyses, particularly Anomaly Detection. A review of the latest efforts in BSM searches with anomaly detection is presented in these proceedings, focusin…

Unsupervised and probabilistic learning with Contrastive Local Learning Networks: The Restricted Kirchhoff Machine
Marcelo Guzman, Simone Ciarella, Andrea J. Liu
https://arxiv.org/abs/2509.15842

Unsupervised and probabilistic learning with Contrastive Local Learning Networks: The Restricted Kirchhoff Machine
Autonomous physical learning systems modify their internal parameters and solve computational tasks without relying on external computation. Compared to traditional computers, they enjoy distributed and energy-efficient learning due to their physical dynamics. In this paper, we introduce a self-learning resistor network, the Restricted Kirchhoff Machine, capable of solving unsupervised learning tasks akin to the Restricted Boltzmann Machine algorithm. The circuit relies on existing technology b…

Machine learning in lattice quantum gravity
Jan Ambjorn, Zbigniew Drogosz, Jakub Gizbert-Studnicki, Andrzej G\"orlich, D\'aniel N\'emeth, Marcus Reitz
https://arxiv.org/abs/2510.02159

Machine learning in lattice quantum gravity
Using numerical data coming from Monte Carlo simulations of four-dimensional Causal Dynamical Triangulations, we study how automated machine learning algorithms can be used to recognize transitions between different phases of quantum geometries observed in lattice quantum gravity. We tested seven supervised and seven unsupervised machine learning models and found that most of them were very successful in that task, even outperforming standard methods based on order parameters.

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/6]:
- OT Score: An OT based Confidence Score for Source Free Unsupervised Domain Adaptation
Yiming Zhang, Sitong Liu, Alex Cloninger

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Unsupervised Domain Adaptation with an Unobservable Source Subpopulation
Chao Ying, Jun Jin, Haotian Zhang, Qinglong Tian, Yanyuan Ma, Yixuan Li, Jiwei Zhao