2.9k citations · 3.2k across the 4 of their papers we have counts for
5 papers
Atomistic graph networks for experimental materials property prediction
Tian Xie, Victor Bapst, Alexander L. Gaunt +5
Machine Learning (ML) has the potential to accelerate discovery of new materials and shed light on useful properties of existing materials. A key difficulty when applying ML in Mat…
A Deep Learning Approach for Characterizing Major Galaxy Mergers
Skanda Koppula, Victor Bapst, Marc Huertas-Company +15
Fine-grained estimation of galaxy merger stages from observations is a key problem useful for validation of our current theoretical understanding of galaxy formation. To this end,…
Game Plan: What AI can do for Football, and What Football can do for AI
Karl Tuyls, Shayegan Omidshafiei, Paul Muller +33
The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball,…
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
Stanislav Nikolov, Sam Blackwell, Alexei Zverovitch +26
Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manua…
The Kinetics Human Action Video Dataset
Will Kay, Joao Carreira, Karen Simonyan +9
We describe the DeepMind Kinetics human action video dataset. The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 1…