Bayesian neural networks harness the power of Bayesian inference which provides an
approach to neural learning that not only focuses on accuracy but also uncertainty …

Neuroevolution is a machine learning method for evolving neural networks parameters and
topology with a high degree of flexibility that makes them applicable to a wide range of …

Deep learning models, such as convolutional neural networks, have long been applied to
image and multi-media tasks, particularly those with structured data. More recently, there …

Autoencoders gained popularity in the deep learning revolution given their ability to
compress data and provide dimensionality reduction. Although prominent deep learning …

The complex and computationally expensive nature of landscape evolution models poses
significant challenges to the inference and optimization of unknown model parameters …

It is well known that canonical recurrent neural networks (RNNs) face limitations in learning
long-term dependencies, which have been addressed by memory structures in long short …

Autoencoders gained popularity in the deep learning revolution given their ability to
compress data and provide dimensionality reduction. Although prominent deep learning …