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2020 – today
- 2024
[i19]Charvi Rastogi, Tian Huey Teh, Pushkar Mishra, Roma Patel, Zoe Ashwood, Aida Mostafazadeh Davani, Mark Diaz, Michela Paganini, Alicia Parrish, Ding Wang, Vinodkumar Prabhakaran, Lora Aroyo, Verena Rieser:
Insights on Disagreement Patterns in Multimodal Safety Perception across Diverse Rater Groups. CoRR abs/2410.17032 (2024)- 2023
- [c5]Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Velickovic:
Neural Algorithmic Reasoning with Causal Regularisation. ICML 2023: 2272-2288 - [i18]Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Velickovic:
Neural Algorithmic Reasoning with Causal Regularisation. CoRR abs/2302.10258 (2023) - [i17]Massimo Caccia, Alexandre Galashov, Arthur Douillard, Amal Rannen-Triki, Dushyant Rao, Michela Paganini, Laurent Charlin, Marc'Aurelio Ranzato, Razvan Pascanu:
Towards Compute-Optimal Transfer Learning. CoRR abs/2304.13164 (2023) - 2022
- [c4]Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Erich Elsen, Laurent Sifre:
Improving Language Models by Retrieving from Trillions of Tokens. ICML 2022: 2206-2240 - [c3]Aidan Clark, Diego de Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake A. Hechtman, Trevor Cai, Sebastian Borgeaud, George van den Driessche, Eliza Rutherford, Tom Hennigan, Matthew J. Johnson, Albin Cassirer, Chris Jones, Elena Buchatskaya, David Budden, Laurent Sifre, Simon Osindero, Oriol Vinyals, Marc'Aurelio Ranzato, Jack W. Rae, Erich Elsen, Koray Kavukcuoglu, Karen Simonyan:
Unified Scaling Laws for Routed Language Models. ICML 2022: 4057-4086 - [i16]Aidan Clark, Diego de Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake A. Hechtman, Trevor Cai, Sebastian Borgeaud, George van den Driessche, Eliza Rutherford, Tom Hennigan, Matthew J. Johnson, Katie Millican, Albin Cassirer, Chris Jones, Elena Buchatskaya, David Budden, Laurent Sifre, Simon Osindero, Oriol Vinyals, Jack W. Rae, Erich Elsen, Koray Kavukcuoglu, Karen Simonyan:
Unified Scaling Laws for Routed Language Models. CoRR abs/2202.01169 (2022) - 2021
- [i15]Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Erich Elsen, Laurent Sifre:
Improving language models by retrieving from trillions of tokens. CoRR abs/2112.04426 (2021) - [i14]Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, H. Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant M. Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent Sifre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d'Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew J. Johnson, Blake A. Hechtman, Laura Weidinger, Iason Gabriel, William Isaac, Edward Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, Geoffrey Irving:
Scaling Language Models: Methods, Analysis & Insights from Training Gopher. CoRR abs/2112.11446 (2021) - 2020
- [c2]Luca Bertinetto, João F. Henriques, Samuel Albanie, Michela Paganini, Gül Varol:
Preface. Preregister@NeurIPS 2020: i - [e1]Luca Bertinetto, João F. Henriques, Samuel Albanie, Michela Paganini, Gül Varol:
NeurIPS 2020 Workshop on Pre-registration in Machine Learning, 11 December 2020, Virtual Event. Proceedings of Machine Learning Research 148, PMLR 2020 [contents] - [i13]Michela Paganini, Jessica Zosa Forde:
On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks. CoRR abs/2001.05050 (2020) - [i12]Michela Paganini, Jessica Zosa Forde:
Streamlining Tensor and Network Pruning in PyTorch. CoRR abs/2004.13770 (2020) - [i11]Michela Paganini, Jessica Zosa Forde:
dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration. CoRR abs/2006.07484 (2020) - [i10]Michela Paganini, Jessica Zosa Forde:
Bespoke vs. Prêt-à-Porter Lottery Tickets: Exploiting Mask Similarity for Trainable Sub-Network Finding. CoRR abs/2007.04091 (2020) - [i9]Michela Paganini:
Prune Responsibly. CoRR abs/2009.09936 (2020)
2010 – 2019
- 2019
- [c1]Ari S. Morcos, Haonan Yu, Michela Paganini, Yuandong Tian:
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers. NeurIPS 2019: 4933-4943 - [i8]Michela Paganini:
Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production. CoRR abs/1903.05082 (2019) - [i7]Jessica Zosa Forde, Michela Paganini:
The Scientific Method in the Science of Machine Learning. CoRR abs/1904.10922 (2019) - [i6]Ari S. Morcos, Haonan Yu, Michela Paganini, Yuandong Tian:
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers. CoRR abs/1906.02773 (2019) - 2018
- [i5]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 M. 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 V. 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, Przemyslaw Karpinski, 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 S. Neubauer, Harvey B. Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel N. 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 Andrew Stewart, Bob Stienen, Ian Stockdale, Giles Chatham 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:
Machine Learning in High Energy Physics Community White Paper. CoRR abs/1807.02876 (2018) - 2017
- [j1]Luke de Oliveira, Michela Paganini, Benjamin Nachman:
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis. Comput. Softw. Big Sci. 1(1) (2017) - [i4]Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Prabhat, Evan Racah:
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC. CoRR abs/1711.03573 (2017) - [i3]Michela Paganini:
Machine Learning Algorithms for b-Jet Tagging at the ATLAS Experiment. CoRR abs/1711.08811 (2017) - [i2]Luke de Oliveira, Michela Paganini, Benjamin Nachman:
Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters. CoRR abs/1711.08813 (2017) - [i1]Michela Paganini, Luke de Oliveira, Benjamin Nachman:
CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks. CoRR abs/1712.10321 (2017)
Coauthor Index
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