Alert button
Picture for Michela Paganini

Michela Paganini

Alert button

dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration

Jun 12, 2020
Michela Paganini, Jessica Zosa Forde

Figure 1 for dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration
Viaarxiv icon

Streamlining Tensor and Network Pruning in PyTorch

Apr 28, 2020
Michela Paganini, Jessica Forde

Figure 1 for Streamlining Tensor and Network Pruning in PyTorch
Viaarxiv icon

On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks

Jan 14, 2020
Michela Paganini, Jessica Forde

Figure 1 for On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks
Figure 2 for On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks
Figure 3 for On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks
Figure 4 for On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks
Viaarxiv icon

One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers

Jun 06, 2019
Ari S. Morcos, Haonan Yu, Michela Paganini, Yuandong Tian

Figure 1 for One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Figure 2 for One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Figure 3 for One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Figure 4 for One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Viaarxiv icon

The Scientific Method in the Science of Machine Learning

Apr 24, 2019
Jessica Zosa Forde, Michela Paganini

Viaarxiv icon

Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production

Mar 12, 2019
Michela Paganini

Viaarxiv icon

Machine Learning in High Energy Physics Community White Paper

Jul 08, 2018
Kim Albertsson, Piero Altoe, Dustin Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Taylor Childers, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Javier Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark Neubauer, Harvey Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Omar Zapata

Figure 1 for Machine Learning in High Energy Physics Community White Paper
Figure 2 for Machine Learning in High Energy Physics Community White Paper
Figure 3 for Machine Learning in High Energy Physics Community White Paper
Viaarxiv icon

CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks

Dec 21, 2017
Michela Paganini, Luke de Oliveira, Benjamin Nachman

Figure 1 for CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
Figure 2 for CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
Figure 3 for CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
Figure 4 for CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
Viaarxiv icon

Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters

Dec 21, 2017
Michela Paganini, Luke de Oliveira, Benjamin Nachman

Figure 1 for Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
Figure 2 for Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
Figure 3 for Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
Viaarxiv icon

Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC

Nov 29, 2017
Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Prabhat, Evan Racah

Figure 1 for Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
Figure 2 for Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
Figure 3 for Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
Figure 4 for Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
Viaarxiv icon