Michela Paganini
Michela Paganini
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Scaling language models: Methods, analysis & insights from training gopher
JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ...
arXiv preprint arXiv:2112.11446, 2021
Improving language models by retrieving from trillions of tokens
S Borgeaud, A Mensch, J Hoffmann, T Cai, E Rutherford, K Millican, ...
arXiv preprint arXiv:2112.04426, 2021
Optimisation and performance studies of the ATLAS b-tagging algorithms for the 2017-18 LHC run
ATLAS collaboration
ATL PHYS PUB 13, 2017, 2017
CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks
M Paganini, L de Oliveira, B Nachman
Physical Review D 97 (1), 014021, 2018
Learning particle physics by example: location-aware generative adversarial networks for physics synthesis
L de Oliveira, M Paganini, B Nachman
Computing and Software for Big Science 1 (1), 4, 2017
Machine learning in high energy physics community white paper
K Albertsson, P Altoe, D Anderson, J Anderson, M Andrews, ...
arXiv preprint arXiv:1807.02876, 2018
Accelerating science with generative adversarial networks: an application to 3D particle showers in multilayer calorimeters
M Paganini, L de Oliveira, B Nachman
Physical review letters 120 (4), 042003, 2018
Measurements of Higgs boson properties in the diphoton decay channel with of collision data at with the ATLAS detector
M Aaboud, G Aad, B Abbott, B Abeloos, SH Abidi, OS AbouZeid, ...
Physical Review D 98 (5), 052005, 2018
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
A Morcos, H Yu, M Paganini, Y Tian
Advances in Neural Information Processing Systems, 4932-4942, 2019
Search for Higgs boson pair production in the yybb final state with 13 TeV pp collision data collected by the ATLAS experiment
A collaboration
Journal of High Energy Physics 2018 (11), 40, 2018
Identification of jets containing b-hadrons with recurrent neural networks at the ATLAS experiment
ATLAS collaboration
ATLAS note: ATL-PHYS-PUB-2017-003, http://cds. cern. ch/record/2255226, 2017
A Roadmap for HEP Software and Computing R&D for the 2020s
J Albrecht, AA Alves, G Amadio, G Andronico, N Anh-Ky, L Aphecetche, ...
Computing and software for big science 3 (1), 1-49, 2019
Controlling physical attributes in gan-accelerated simulation of electromagnetic calorimeters
L de Oliveira, M Paganini, B Nachman
Journal of Physics: Conference Series 1085 (4), 042017, 2018
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
W Bhimji, SA Farrell, T Kurth, M Paganini, E Racah
arXiv preprint arXiv:1711.03573, 2017
Search for Higgs boson pair production in the bbγγ final state using pp collision data at√ s= 13 TeV with the ATLAS detector
ATLAS collaboration
ATLAS-CONF-2016-004, 2016
The scientific method in the science of machine learning
JZ Forde, M Paganini
arXiv preprint arXiv:1904.10922, 2019
Electromagnetic showers beyond shower shapes
L De Oliveira, B Nachman, M Paganini
Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2020
Unified Scaling Laws for Routed Language Models
A Clark, D Casas, A Guy, A Mensch, M Paganini, J Hoffmann, B Damoc, ...
arXiv preprint arXiv:2202.01169, 2022
Prune Responsibly
M Paganini
arXiv preprint arXiv:2009.09936, 2020
Machine Learning Algorithms for b-Jet Tagging at the ATLAS Experiment
M Paganini
Journal of Physics: Conference Series 1085 (4), 042031, 2018
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