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Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment, 2020-02, Vol.952, p.161804, Article 161804

2020

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Deep Learning for direct Dark Matter search with nuclear emulsions

Computer physics communications, 2022-06, Vol.275, p.108312, Article 108312

2022

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The TrackML high-energy physics tracking challenge on Kaggle

EPJ Web of Conferences, 2019, Vol.214, p.6037

2019

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LHCb Topological Trigger Reoptimization

Journal of physics. Conference series, 2015-12, Vol.664 (8), p.82025

2015

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Deep learning for Directional Dark Matter search

Journal of physics. Conference series, 2020-04, Vol.1525 (1), p.12108

2020

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Vertex and energy reconstruction in JUNO with machine learning methods

Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment, 2021-09, Vol.1010, p.165527, Article 165527

2021

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Segmentation of EM showers for neutrino experiments with deep graph neural networks

Journal of instrumentation, 2021-12, Vol.16 (12), p.P12035

2021

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The DL Advocate: playing the devil’s advocate with hidden systematic uncertainties

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Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problems

Generalization of change-point detection in time series data based on direct density ratio estimation

SANgo: a storage infrastructure simulator with reinforcement learning support

Dataset of tau neutrino interactions recorded by the OPERA experiment

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Deep Learning for direct Dark Matter search with nuclear emulsions

The TrackML high-energy physics tracking challenge on Kaggle

LHCb Topological Trigger Reoptimization

Deep learning for Directional Dark Matter search

Vertex and energy reconstruction in JUNO with machine learning methods

Segmentation of EM showers for neutrino experiments with deep graph neural networks

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