International audienceThe use of neural networks is still difficult in many application areas due to the lack of explanation facilities (the " black box " problem). An example of such applications is multiple criteria decision making (MCDM), applied to location problems having environmental impact. However, the concepts and methods presented are also applicable to other problem domains. These concepts show how to extract explanations from neural networks that are easily understandable for the user. Explanations obtained may in many cases even be better than those of expert systems. The INKA network presented in this paper is well adapted for MCDM problems, while also having properties that simplify the extraction of explanations compared to...
Deep neural networks (DNNs) are an indispensable machine learning tool despite the difficulty of dia...
Despite their success-story, artificial neural networks have one major disadvantage compared to othe...
Accepted at IJCAI19 Neural-Symbolic Learning and Reasoning Workshop (https://sites.google.com/view/n...
The opaqueness of deep neural networks hinders their employment in safety-critical applications. Thi...
Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a b...
Explanation is an important function in symbolic artificial intelligence (AI). For instance, explana...
Active research into processes and techniques for extracting the knowledge embedded within trained a...
Artificial Intelligence (AI) is increasingly affecting people’s lives. AI is even employed in fields...
This paper proposes a theoretical analysis of one approach to the eXplainable AI (XAI) problem, usin...
The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explor...
Explanations of the decisions made by a deep neural network are important for human end-users to be ...
We investigate the potential of Neural-Symbolic integration to reason about what a neural network ha...
The large and still increasing popularity of deep learning clashes with a major limit of neural netw...
An important drawback of many artificial neural networks (ANN) is their lack of explanation capabili...
Deep neural networks (DNNs) are an indispensable machine learning tool despite the difficulty of dia...
Deep neural networks (DNNs) are an indispensable machine learning tool despite the difficulty of dia...
Despite their success-story, artificial neural networks have one major disadvantage compared to othe...
Accepted at IJCAI19 Neural-Symbolic Learning and Reasoning Workshop (https://sites.google.com/view/n...
The opaqueness of deep neural networks hinders their employment in safety-critical applications. Thi...
Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a b...
Explanation is an important function in symbolic artificial intelligence (AI). For instance, explana...
Active research into processes and techniques for extracting the knowledge embedded within trained a...
Artificial Intelligence (AI) is increasingly affecting people’s lives. AI is even employed in fields...
This paper proposes a theoretical analysis of one approach to the eXplainable AI (XAI) problem, usin...
The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explor...
Explanations of the decisions made by a deep neural network are important for human end-users to be ...
We investigate the potential of Neural-Symbolic integration to reason about what a neural network ha...
The large and still increasing popularity of deep learning clashes with a major limit of neural netw...
An important drawback of many artificial neural networks (ANN) is their lack of explanation capabili...
Deep neural networks (DNNs) are an indispensable machine learning tool despite the difficulty of dia...
Deep neural networks (DNNs) are an indispensable machine learning tool despite the difficulty of dia...
Despite their success-story, artificial neural networks have one major disadvantage compared to othe...
Accepted at IJCAI19 Neural-Symbolic Learning and Reasoning Workshop (https://sites.google.com/view/n...