Prabhat

TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.

TL;DR: In this article, the authors explore the use of neural networks as an alternative to GPs to model distributions over functions, and show that performing adaptive basis function regression with a neural network as the parametric form performs competitively with state-of-the-art GP-based approaches, but scales linearly with the number of data rather than cubically.

TL;DR: This work shows that performing adaptive basis function regression with a neural network as the parametric form performs competitively with state-of-the-art GP-based approaches, but scales linearly with the number of data rather than cubically, which allows for a previously intractable degree of parallelism.

TL;DR: This work developed deep Convolutional Neural Network (CNN) classification system and demonstrated the usefulness of Deep Learning technique for tackling climate pattern detection problems and teamed with Bayesian based hyper-parameter optimization scheme.

TL;DR: The current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1-week) extreme precipitation events over North America is surveyed in this article.