Search Results for author: Luke de Oliveira

Found 7 papers, 4 papers with code

Emergent Properties of Finetuned Language Representation Models

no code implementations23 Oct 2019 Alexandre Matton, Luke de Oliveira

Large, self-supervised transformer-based language representation models have recently received significant amounts of attention, and have produced state-of-the-art results across a variety of tasks simply by scaling up pre-training on larger and larger corpora.

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

3 code implementations21 Dec 2017 Michela Paganini, Luke de Oliveira, Benjamin Nachman

The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measurements.

Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters

no code implementations23 Nov 2017 Luke de Oliveira, Michela Paganini, Benjamin Nachman

High-precision modeling of subatomic particle interactions is critical for many fields within the physical sciences, such as nuclear physics and high energy particle physics.

Attribute Generative Adversarial Network

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

4 code implementations5 May 2017 Michela Paganini, Luke de Oliveira, Benjamin Nachman

Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of particle collisions to build expectations of what experimental data may look like under different theory modeling assumptions.

Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

3 code implementations20 Jan 2017 Luke de Oliveira, Michela Paganini, Benjamin Nachman

We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images -- 2D representations of energy depositions from particles interacting with a calorimeter.

Generative Adversarial Network

Jet-Images -- Deep Learning Edition

1 code implementation16 Nov 2015 Luke de Oliveira, Michael Kagan, Lester Mackey, Benjamin Nachman, Ariel Schwartzman

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons.

Jet Tagging

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