Michela Paganini

Orcid: 0000-0003-4102-8002

According to our database1, Michela Paganini authored at least 21 papers between 2017 and 2023.

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Bibliography

2023
Towards Compute-Optimal Transfer Learning.
CoRR, 2023

Neural Algorithmic Reasoning with Causal Regularisation.
Proceedings of the International Conference on Machine Learning, 2023

2022
Unified Scaling Laws for Routed Language Models.
CoRR, 2022



2021
Scaling Language Models: Methods, Analysis & Insights from Training Gopher.
CoRR, 2021

2020
Prune Responsibly.
CoRR, 2020

Bespoke vs. Prêt-à-Porter Lottery Tickets: Exploiting Mask Similarity for Trainable Sub-Network Finding.
CoRR, 2020

dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration.
CoRR, 2020

Streamlining Tensor and Network Pruning in PyTorch.
CoRR, 2020

On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks.
CoRR, 2020

Preface.
Proceedings of the NeurIPS 2020 Workshop on Pre-registration in Machine Learning, 2020

2019
The Scientific Method in the Science of Machine Learning.
CoRR, 2019

Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production.
CoRR, 2019

One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
Machine Learning in High Energy Physics Community White Paper.
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CoRR, 2018

2017
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis.
Comput. Softw. Big Sci., November, 2017

CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks.
CoRR, 2017

Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters.
CoRR, 2017

Machine Learning Algorithms for b-Jet Tagging at the ATLAS Experiment.
CoRR, 2017

Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC.
CoRR, 2017


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