375 citations · 1.3k across the 8 of their papers we have counts for
8 papers
Emergent Properties of Finetuned Language Representation Models
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…
Repurposing Decoder-Transformer Language Models for Abstractive Summarization
Luke de Oliveira, Alfredo Láinez Rodrigo
Neural network models have shown excellent fluency and performance when applied to abstractive summarization. Many approaches to neural abstractive summarization involve the introd…
Electromagnetic Showers Beyond Shower Shapes
Luke de Oliveira, Benjamin Nachman, Michela Paganini
Correctly identifying the nature and properties of outgoing particles from high energy collisions at the Large Hadron Collider is a crucial task for all aspects of data analysis. C…
CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
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 measu…
Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters
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. Most…
Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
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 theo…
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
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 Ad…
Jet-Images -- Deep Learning Edition
Luke de Oliveira, Michael Kagan, Lester Mackey +2
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 potentia…