542 citations · 704 across the 35 of their papers we have counts for
9 papers · 1 filter
Where Did Your Model Learn That? Label-free Influence for Self-supervised Learning
Nidhin Harilal, Amit Kiran Rege, Reza Akbarian Bafghi +2
Self-supervised learning (SSL) has revolutionized learning from large-scale unlabeled datasets, yet the intrinsic relationship between pretraining data and the learned representati…
ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecasting
Guillaume Couairon, Renu Singh, Anastase Charantonis +2
Weather forecasting plays a vital role in today's society, from agriculture and logistics to predicting the output of renewable energies, and preparing for extreme weather events.…
Machine Learning for the Physics of Climate
Annalisa Bracco, Julien Brajard, Henk A. Dijkstra +3
An exponential growth in computing power, which has brought more sophisticated and higher resolution simulations of the climate system, and an exponential increase in observations…
Leveraging deterministic weather forecasts for in-situ probabilistic temperature predictions via deep learning
David Landry, Anastase Charantonis, Claire Monteleoni
We propose a neural network approach to produce probabilistic weather forecasts from a deterministic numerical weather prediction. Our approach is applied to operational surface te…
MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised Representations
Reza Akbarian Bafghi, Nidhin Harilal, Claire Monteleoni +1
This paper introduces MixDiff, a new self-supervised learning (SSL) pre-training framework that combines real and synthetic images. Unlike traditional SSL methods that predominantl…
ArchesWeather: An efficient AI weather forecasting model at 1.5° resolution
Guillaume Couairon, Christian Lessig, Anastase Charantonis +1
One of the guiding principles for designing AI-based weather forecasting systems is to embed physical constraints as inductive priors in the neural network architecture. A popular…