29 citations · 99 across the 7 of their papers we have counts for
8 papers
Precipitation Downscaling with Spatiotemporal Video Diffusion
Prakhar Srivastava, Ruihan Yang, Gavin Kerrigan +4
In climate science and meteorology, high-resolution local precipitation (rain and snowfall) predictions are limited by the computational costs of simulation-based methods. Statisti…
ACE: A fast, skillful learned global atmospheric model for climate prediction
Oliver Watt-Meyer, Gideon Dresdner, Jeremy McGibbon +9
Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a…
Learning to correct spectral methods for simulating turbulent flows
Gideon Dresdner, Dmitrii Kochkov, Peter Norgaard +4
Despite their ubiquity throughout science and engineering, only a handful of partial differential equations (PDEs) have analytical, or closed-form solutions. This motivates a vast…
Neighborhood Contrastive Learning Applied to Online Patient Monitoring
Hugo Yèche, Gideon Dresdner, Francesco Locatello +2
Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monito…
Boosting Variational Inference With Locally Adaptive Step-Sizes
Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa +2
Variational Inference makes a trade-off between the capacity of the variational family and the tractability of finding an approximate posterior distribution. Instead, Boosting Vari…
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
Geoffrey Négiar, Gideon Dresdner, Alicia Tsai +4
We propose a novel Stochastic Frank-Wolfe (a.k.a. conditional gradient) algorithm for constrained smooth finite-sum minimization with a generalized linear prediction/structure. Thi…
Scalable Gaussian Processes on Discrete Domains
Vincent Fortuin, Gideon Dresdner, Heiko Strathmann +1
Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data. Scalable kernel me…
Boosting Black Box Variational Inference
Francesco Locatello, Gideon Dresdner, Rajiv Khanna +2
Approximating a probability density in a tractable manner is a central task in Bayesian statistics. Variational Inference (VI) is a popular technique that achieves tractability by…