13 citations · 13 across the 5 of their papers we have counts for
5 papers
Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
Anna Asch, Raphael Rossellini, Pedram Hassanzadeh +1
Probabilistic weather forecasting is undergoing rapid transformation with artificial intelligence (AI). In traditional numerical weather prediction, computing power can limit how w…
How Private is Your Attention? Bridging Privacy with In-Context Learning
Soham Bonnerjee, Zhen Wei, Yeon +3
In-context learning (ICL)-the ability of transformer-based models to perform new tasks from examples provided at inference time-has emerged as a hallmark of modern language models.…
Riemannian Residual Neural Networks
Isay Katsman, Eric Ming Chen, Sidhanth Holalkere +4
Recent methods in geometric deep learning have introduced various neural networks to operate over data that lie on Riemannian manifolds. Such networks are often necessary to learn…
Slow passage through the Busse balloon -- predicting steps on the Eckhaus staircase
Anna Asch, Montie Avery, Anthony Cortez +1
Motivated by the impact of worsening climate conditions on vegetation patches, we study dynamic instabilities in an idealized Ginzburg-Landau model. Our main results predict time i…
Model-assisted deep learning of rare extreme events from partial observations
Anna Asch, Ethan Brady, Hugo Gallardo +3
To predict rare extreme events using deep neural networks, one encounters the so-called small data problem because even long-term observations often contain few extreme events. Her…