Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …

From an environmental standpoint, there are a few crucial aspects of training a neural
network that have a major impact on the quantity of carbon that it emits. These factors …

Abstract Supervised Pix2Pix and unsupervised Cycle-consistency are two modes that
dominate the field of medical image-to-image translation. However, neither modes are ideal …

Using a knowledge-attitudes-behavior practice (KABP) paradigm, professionals have
focused on educating the public in biomedical explanations of mental illness. Especially in …

Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious
task performed by on-the-ground emergency responders and analysts. Social media has …

Physical simulations produce excellent predictions of weather effects. Neural radiance fields
produce SOTA scene models. We describe a novel NeRF-editing procedure that can fuse …

Natural disasters, such as floods, tornadoes, or wildfires, are increasingly pervasive as the
Earth undergoes global warming. It is difficult to predict when and where an incident will …

Progress in ML in the last decade has been extraordinary and has rekindled the notion that
AI systems could eventually reach human levels of performance, which was abandoned for …

Deep Learning techniques have been increasingly used in flood risk management to
overcome the limitations of accurate, yet slow, numerical models, and to improve the results …