15 citations · 20 across the 3 of their papers we have counts for
4 papers
Predicting ice flow using machine learning
Yimeng Min, S. Karthik Mukkavilli, Yoshua Bengio
Though machine learning has achieved notable success in modeling sequential and spatial data for speech recognition and in computer vision, applications to remote sensing and clima…
Deep learning for Aerosol Forecasting
Caleb Hoyne, S. Karthik Mukkavilli, David Meger
Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth system, are prone to biases against grou…
Tackling Climate Change with Machine Learning
David Rolnick, Priya L. Donti, Lynn H. Kaack +19
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a po…
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
Victor Schmidt, Alexandra Luccioni, S. Karthik Mukkavilli +4
We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs).…