The Importance of Architecture Choice in Deep Learning for Climate Applications

21 February 2024

Papers citing "The Importance of Architecture Choice in Deep Learning for Climate Applications"

8 / 8 papers shown

Title
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics Mariana C. A. Clare Maike Sonnewald Redouane Lguensat Julie Deshayes Venkatramani Balaji BDL 76 31 0 30 Apr 2022
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators Jaideep Pathak Shashank Subramanian P. Harrington S. Raja Ashesh Chattopadhyay ... Zong-Yi Li Kamyar Azizzadenesheli Pedram Hassanzadeh K. Kashinath Anima Anandkumar AI4Cl 144 674 0 22 Feb 2022
Bridging observation, theory and numerical simulation of the ocean using Machine Learning Maike Sonnewald Redouane Lguensat Daniel C. Jones P. Dueben J. Brajard Venkatramani Balaji AI4Cl AI4CE 60 100 0 26 Apr 2021
Will Artificial Intelligence supersede Earth System and Climate Models? C. Irrgang Niklas Boers Maike Sonnewald E. Barnes C. Kadow J. Staneva J. Saynisch‐Wagner AI4Cl AI4CE 37 183 0 22 Jan 2021
Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users Laurent Valentin Jospin Wray Buntine F. Boussaïd Hamid Laga Bennamoun OOD BDL UQCV 68 620 0 14 Jul 2020
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 538 21,613 0 22 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 123 3,848 0 10 Apr 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 582 16,828 0 16 Feb 2016