91 citations · 93 across the 5 of their papers we have counts for
5 papers
Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations
Gabriëlle Ras, Luca Ambrogioni, Pim Haselager +2
In this paper we introduce the temporally factorized 3D convolution (3TConv) as an interpretable alternative to the regular 3D convolution (3DConv). In a 3TConv the 3D convolutiona…
Explainable Deep Learning: A Field Guide for the Uninitiated
Gabrielle Ras, Ning Xie, Marcel van Gerven +1
Deep neural networks (DNNs) have become a proven and indispensable machine learning tool. As a black-box model, it remains difficult to diagnose what aspects of the model's input d…
Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks
Gabriëlle Ras, Ron Dotsch, Luca Ambrogioni +2
Perceived personality traits attributed to an individual do not have to correspond to their actual personality traits and may be determined in part by the context in which one enco…
Temporal Factorization of 3D Convolutional Kernels
Gabriëlle Ras, Luca Ambrogioni, Umut Güçlü +1
3D convolutional neural networks are difficult to train because they are parameter-expensive and data-hungry. To solve these problems we propose a simple technique for learning 3D…
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
Gabrielle Ras, Marcel van Gerven, Pim Haselager
Issues regarding explainable AI involve four components: users, laws & regulations, explanations and algorithms. Together these components provide a context in which explanation me…