40 citations · 77 across the 24 of their papers we have counts for
15 papers · 1 filter
In-Context Reinforcement Learning through Bayesian Fusion of Context and Value Prior
Anaïs Berkes, Vincent Taboga, Donna Vakalis +2
In-context reinforcement learning (ICRL) promises fast adaptation to unseen environments without parameter updates, but current methods either cannot improve beyond the training di…
On Global Applicability and Location Transferability of Generative Deep Learning Models for Precipitation Downscaling
Paula Harder, Christian Lessig, Matthew Chantry +2
Deep learning offers promising capabilities for the statistical downscaling of climate and weather forecasts, with generative approaches showing particular success in capturing fin…
CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations
Hager Radi Abdelwahed, Mélisande Teng, Robin Zbinden +4
Species distribution models (SDMs) are widely used to predict species' geographic distributions, serving as critical tools for ecological research and conservation planning. Typica…
The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions
Devin Kwok, Gül Sena Altıntaş, Colin Raffel +1
Neural network training is inherently sensitive to initialization and the randomness induced by stochastic gradient descent. However, it is unclear to what extent such effects lead…
Evaluating the transferability potential of deep learning models for climate downscaling
Ayush Prasad, Paula Harder, Qidong Yang +4
Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding and adapting to climate change at region…
Improving Molecular Modeling with Geometric GNNs: an Empirical Study
Ali Ramlaoui, Théo Saulus, Basile Terver +4
Rapid advancements in machine learning (ML) are transforming materials science by significantly speeding up material property calculations. However, the proliferation of ML approac…