Publications

(2020). Studying the parton content of the proton with deep learning models. Artificial Intelligence for Science, Industry and Society (AISIS2019) Mexico City, Mexico, October 21-25, 2019.

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(2019). Modelling conditional probabilities with Riemann-Theta Boltzmann Machines. 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research: Empowering the revolution: Bringing Machine Learning to High Performance Computing (ACAT 2019) Saas-Fee, Switzerland, March 11-15, 2019.

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(2017). Towards a Neural Network determination of Pion Fragmentation Functions. Proceedings, Parton Radiation and Fragmentation from LHC to FCC-ee: CERN, Geneva, Switzerland, November 22-23, 2016.

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(2017). Modeling NNLO jet corrections with neural networks. Proceedings, 23rd Cracow Epiphany Conference on Particle Theory Meets the First Data from LHC Run 2: Cracow, Poland, January 9-12, 2017.

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(2017). Minimisation strategies for the determination of parton density functions. 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) Seattle, WA, USA, August 21-25, 2017.

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(2017). Machine learning challenges in theoretical HEP. 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) Seattle, WA, USA, August 21-25, 2017.

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(2016). Towards the compression of parton densities through machine learning algorithms. 51st Rencontres de Moriond on QCD and High Energy Interactions La Thuile, Italy, March 19-26, 2016.

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(2016). SMPDF Web: a web-based application for specialized minimal parton distribution functions. 7th Workshop italiano sulla fisica pp a LHC (pp @ LHC 2016) Roma, Italy, May 16-18, 2016.

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(2016). Les Houches 2015: Physics at TeV Colliders Standard Model Working Group Report. 9th Les Houches Workshop on Physics at TeV Colliders (PhysTeV 2015) Les Houches, France, June 1-19, 2015.

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(2015). Doped Parton Distributions. 27th Rencontres de Blois on Particle Physics and Cosmology Blois, France, May 31-June 5, 2015.

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