Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

This paper provides an entry point to the problem of interpreting a deep neural network
model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. As a …

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

We show that machine learning methods, in particular, extreme trees and neural networks
(NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts …

Graph Networks are used to make decisions in potentially complex scenarios but it is usually
not obvious how or why they made them. In this work, we study the explainability of Graph …

Weakly-supervised object localization (WSOL) has gained popularity over the last years for
its promise to train localization models with only image-level labels. Since the seminal …

The amount of transported sediment load by streams is a vital but high nonlinear dynamic
process in water resources management. In the current paper, two optimum predictive …

Classification is one of the most active research and application areas of neural networks.
The literature is vast and growing. This paper summarizes some of the most important …

Convinced by the predictive quality of artificial neural network (ANN) models in ecology, we
have turned our interests to their explanatory capacities. Seven methods which can give the …

Information security is an issue of serious global concern. The complexity, accessibility, and
openness of the Internet have served to increase the security risk of information systems …