The Good, the Bad and the Ugly: Augmenting a black-box model with expert knowledge

07/24/2019
by   Raoul Heese, et al.
0

We address a non-unique parameter fitting problem in the context of material science. In particular, we propose to resolve ambiguities in parameter space by augmenting a black-box artificial neural network (ANN) model with two different levels of expert knowledge and benchmark them against a pure black-box model.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/04/2010

Faster Black-Box Algorithms Through Higher Arity Operators

We extend the work of Lehre and Witt (GECCO 2010) on the unbiased black-...
research
09/21/2023

Neural Modelling of Dynamic Systems with Time Delays Based on an Adjusted NEAT Algorithm

A problem related to the development of an algorithm designed to find an...
research
09/21/2017

Defining a Lingua Franca to Open the Black Box of a Naïve Bayes Recommender

Many AI systems have a black box nature that makes it difficult to under...
research
04/23/2018

Rendition: Reclaiming what a black box takes away

The premise of our work is deceptively familiar: A black box f(·) has al...
research
10/18/2022

It's a long way! Layer-wise Relevance Propagation for Echo State Networks applied to Earth System Variability

Artificial neural networks (ANNs) are known to be powerful methods for m...
research
03/21/2023

Do intermediate feature coalitions aid explainability of black-box models?

This work introduces the notion of intermediate concepts based on levels...
research
08/23/2022

Anomaly Attribution with Likelihood Compensation

This paper addresses the task of explaining anomalous predictions of a b...

Please sign up or login with your details

Forgot password? Click here to reset