Tootfinder

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.

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…

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

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…

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…

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…

Unsupervised machine learning classification of gamma-ray bursts based on the rest-frame prompt emission parameters
Si-Yuan Zhu, Lang Shao, Pak-Hin Thomas Tam, Fu-Wen Zhang
https://arxiv.org/abs/2509.08224

Unsupervised machine learning classification of gamma-ray bursts based on the rest-frame prompt emission parameters
Gamma-ray bursts (GRBs) are generally believed to originate from two distinct progenitors, compact binary mergers and massive collapsars. Traditional and some recent machine learning-based classification schemes predominantly rely on observer-frame physical parameters, which are significantly affected by the redshift effects and may not accurately represent the intrinsic properties of GRBs. In particular, the progenitors usually could only be decided by successful detection of the multi-band lo…

Comparing unsupervised learning methods for local structural identification in colloidal systems
Alptu\u{g} Ulug\"ol, Jessi B\"uckmann, Ruizhi Yang, Roy Hoitink, Alfons van Blaaderen, Frank Smallenburg, Laura Filion
https://arxiv.org/abs/2509.07186

Comparing unsupervised learning methods for local structural identification in colloidal systems
Quantifying local structures in self-assembled systems is a central challenge in soft matter and materials science. When no a priori knowledge of the relevant structures is available, traditional order parameters often fall short. Unsupervised machine learning provides a convenient route to autonomously uncover structural motifs directly from particle configurations. In this work, we systematically compare three popular dimensionality reduction techniques; Principal Component Analysis (PCA), Au…

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…

Crosslisted article(s) found for cond-mat.mtrl-sci. https://arxiv.org/list/cond-mat.mtrl-sci/new
[1/1]:
- Unsupervised Atomic Data Mining via Multi-Kernel Graph Autoencoders for Machine Learning Force Fi...
Hong Sun, Joshua A. Vita, Amit Samanta, Vincenzo Lordi

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

Tidal Tails and Their Dynamics in Open Clusters Using Gaia DR3
Ira Sharma, Vikrant V. Jadhav, Annapurni Subramaniam
https://arxiv.org/abs/2509.09279 https://

Tidal Tails and Their Dynamics in Open Clusters Using Gaia DR3
This research presents unsupervised machine learning and statistical techniques to detect and analyze tidal tails in five open clusters, BH 164, Alessi 2, NGC 2281, NGC 2354, and M67, with ages ranging from 60 Myr to 4 Gyr, using Gaia DR3 data. Color-magnitude diagram (CMD) matching, principal component analysis (PCA), and density-based clustering were used to detect the tails. Tidal tails were found in all clusters, extending 40 to 100 pc and containing 100 to 200 stars. Other statistical meth…

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