Predicting ice flow using machine learning

no code implementations • 20 Oct 2019 • 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 climate science problems are seldom considered.

BIG-bench Machine Learning Management +3

Deep learning for Aerosol Forecasting

no code implementations • 14 Oct 2019 • 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 ground truth.

Tackling Climate Change with Machine Learning

3 code implementations • 10 Jun 2019 • David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help.

BIG-bench Machine Learning Management

Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks

no code implementations • 2 May 2019 • Victor Schmidt, Alexandra Luccioni, S. Karthik Mukkavilli, Narmada Balasooriya, Kris Sankaran, Jennifer Chayes, Yoshua Bengio

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).