no code implementations • 23 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.
no code implementations • NeurIPS Workshop Document_Intelligen 2019 • Luke de Oliveira, Alfredo Láinez Rodrigo
Neural network models have shown excellent fluency and performance when applied to abstractive summarization.
3 code implementations • 21 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.
no code implementations • 23 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.
4 code implementations • 5 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.
3 code implementations • 20 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.
1 code implementation • 16 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.