Plants play a crucial role in supplying food globally. Various environmental factors lead to
plant diseases which results in significant production losses. However, manual detection of …

Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

Abstract Recurrent Neural Networks (RNNs) have become competitive forecasting methods,
as most notably shown in the winning method of the recent M4 competition. However …

A normalizing flow models a complex probability density as an invertible transformation of a
simple base density. Flows based on either coupling or autoregressive transforms both offer …

Planning has been very successful for control tasks with known environment dynamics. To
leverage planning in unknown environments, the agent needs to learn the dynamics from …

Pyro is a probabilistic programming language built on Python as a platform for develo**
advanced probabilistic models in AI research. To scale to large data sets and high …

Abstract Neural Networks (NNs) have provided state-of-the-art results for many challenging
machine learning tasks such as detection, regression and classification across the domains …

Wildfires are a worldwide natural disaster causing important economic damages and loss of
lives. Experts predict that wildfires will increase in the coming years mainly due to climate …

During the past five years the Bayesian deep learning community has developed
increasingly accurate and efficient approximate inference procedures that allow for …

NumPyro is a lightweight library that provides an alternate NumPy backend to the Pyro
probabilistic programming language with the same modeling interface, language primitives …