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The \textsc{Gerda} experiment searches for the neutrinoless double beta ($0\nu\beta$beta$) decay of $^{76}$Ge using high-purity germanium detectors made of material enriched in $^{76}$Ge. For Phase II of the experiment a sensitivity for the…

The MAJORANA DEMONSTRATOR will search for the neutrinoless double-beta decay of the 76Ge isotope with a mixed array of enriched and natural germanium detectors. The observation of this rare decay would indicate the neutrino is its own…

Neutrinoless double beta decay ($0\nu\beta\beta$) is a rare process which could take place if neutrinos are Majorana fermions: the observation of this decay would provide unambiguous evidence for the existence of new Physics beyond the…

Nuclear Experiment · Physics 2026-01-14 Giovanna Saleh

In order to develop reliable services using machine learning, it is important to understand the uncertainty of the model outputs. Often the probability distribution that the prediction target follows has a complex shape, and a mixture…

Machine Learning · Computer Science 2021-05-11 Ryuichi Kanoh , Tomu Yanabe

Interpretability has become incredibly important as machine learning is increasingly used to inform consequential decisions. We propose to construct global explanations of complex, blackbox models in the form of a decision tree…

Machine Learning · Computer Science 2019-01-28 Osbert Bastani , Carolyn Kim , Hamsa Bastani

The Standard Model of particle physics cannot explain the dominance of matter over anti-matter in our Universe. In many model extensions this is a very natural consequence of neutrinos being their own anti-particles (Majorana particles)…

Nuclear Experiment · Physics 2017-04-07 M. Agostini , M. Allardt , A. M. Bakalyarov , M. Balata , I. Barabanov , L. Baudis , C. Bauer , E. Bellotti , S. Belogurov , S. T. Belyaev , G. Benato , A. Bettini , L. Bezrukov , T. Bode , D. Borowicz , V. Brudanin , R. Brugnera , A. Caldwell , C. Cattadori , A. Chernogorov , V. D'Andrea , E. V. Demidova , N. DiMarco , A. diVacri , A. Domula , E. Doroshkevich , V. Egorov , R. Falkenstein , O. Fedorova , K. Freund , N. Frodyma , A. Gangapshev , A. Garfagnini , C. Gooch , P. Grabmayr , V. Gurentsov , K. Gusev , J. Hakenmüller , A. Hegai , M. Heisel , S. Hemmer , W. Hofmann , M. Hult , L. V. Inzhechik , J. Janicskó Csáthy , J. Jochum , M. Junker , V. Kazalov , T. Kihm , I. V. Kirpichnikov , A. Kirsch , A. Kish , A. Klimenko , R. Kneißl , K. T. Knöpfle , O. Kochetov , V. N. Kornoukhov , V. V. Kuzminov , M. Laubenstein , A. Lazzaro , V. I. Lebedev , B. Lehnert , H. Y. Liao , M. Lindner , I. Lippi , A. Lubashevskiy , B. Lubsandorzhiev , G. Lutter , C. Macolino , B. Majorovits , W. Maneschg , E. Medinaceli , M. Miloradovic , R. Mingazheva , M. Misiaszek , P. Moseev , I. Nemchenok , D. Palioselitis , K. Panas , L. Pandola , K. Pelczar , A. Pullia , S. Riboldi , N. Rumyantseva , C. Sada , F. Salamida , M. Salathe , C. Schmitt , B. Schneider , S. Schönert , J. Schreiner , O. Schulz , A. -K. Schütz , B. Schwingenheuer , O. Selivanenko , E. Shevchik , M. Shirchenko , H. Simgen , A. Smolnikov , L. Stanco , L. Vanhoefer , A. A. Vasenko , A. Veresnikova , K. von Sturm , V. Wagner , M. Walter , A. Wegmann , T. Wester , C. Wiesinger , M. Wojcik , E. Yanovich , I. Zhitnikov , S. V. Zhukov , D. Zinatulina , K. Zuber , G. Zuzel

The rapid evolution of machine learning (ML) has led to the widespread adoption of complex "black box" models, such as deep neural networks and ensemble methods. These models exhibit exceptional predictive performance, making them…

Machine Learning · Computer Science 2025-03-28 Moncef Garouani , Josiane Mothe , Ayah Barhrhouj , Julien Aligon

Tree ensembles, such as random forests and boosted trees, are renowned for their high prediction performance. However, their interpretability is critically limited due to the enormous complexity. In this study, we present a method to make a…

Machine Learning · Statistics 2017-03-01 Satoshi Hara , Kohei Hayashi

Deep learning-based discriminative classifiers, despite their remarkable success, remain vulnerable to adversarial examples that can mislead model predictions. While adversarial training can enhance robustness, it fails to address the…

Computer Vision and Pattern Recognition · Computer Science 2025-12-09 Chunheng Zhao , Pierluigi Pisu , Gurcan Comert , Negash Begashaw , Varghese Vaidyan , Nina Christine Hubig

Ensembles of decision trees perform well on many problems, but are not interpretable. In contrast to existing approaches in interpretability that focus on explaining relationships between features and predictions, we propose an alternative…

Machine Learning · Statistics 2020-08-26 Sarah Tan , Matvey Soloviev , Giles Hooker , Martin T. Wells

The MAJORANA Collaboration has completed construction and is now operating an array of high purity Ge detectors searching for neutrinoless double-beta decay ($0\nu\beta\beta$) in $^{76}$Ge. The array, known as the MAJORANA DEMONSTRATOR, is…

Combined bounds on the Majorana neutrino mass for light and heavy neutrino exchange mechanisms are derived from current neutrinoless double beta decay (0{\nu}\b{eta}\b{eta}) search results for a variety of nuclear matrix element (NME)…

High Energy Physics - Experiment · Physics 2021-07-14 Steven D. Biller

The ability to interpret machine learning models has become increasingly important now that machine learning is used to inform consequential decisions. We propose an approach called model extraction for interpreting complex, blackbox…

Machine Learning · Computer Science 2018-03-14 Osbert Bastani , Carolyn Kim , Hamsa Bastani

Prostate cancer being one of the frequently diagnosed malignancy in men, the rising demand for biopsies places a severe workload on pathologists. The grading procedure is tedious and subjective, motivating the development of automated…

Computer Vision and Pattern Recognition · Computer Science 2026-03-06 Riddhasree Bhattacharyya , Pallabi Dutta , Sushmita Mitra