Research Output per year
Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.
Learning systems
Engineering & Materials Science
heuristics
Social Sciences
Conservation
Engineering & Materials Science
Visualization
Engineering & Materials Science
Experiments
Engineering & Materials Science
Human computer interaction
Engineering & Materials Science
learning
Social Sciences
Ecology
Engineering & Materials Science
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Research Output 2016 2019
BIR: A Method for Selecting the Best Interpretable Multidimensional Scaling Rotation using External Variables
Marion, R., Bibal, A. & Frenay, B., 4 Feb 2019, In : Neurocomputing.Research output: Contribution to journal › Article
Ecology
Invariance
Psychology
User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning
Bibal, A., Dumas, B. & Frenay, B., 2019, EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence. MetzResearch output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution
Open Access
File
Learning systems
Human computer interaction
Experiments
Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features
Bibal, A., Marion, R. & Frenay, B., 2018, 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, p. 537-542 7 p.Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution
Open Access
File
Interpretability of Machine Learning Models and Representations: an Introduction
Bibal, A. & Frenay, B., 2016, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, p. 77-82Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution
Open Access
File
heuristics
learning
Introduction to Interpretability in Machine Learning
Bibal, A. & Frenay, B., 2016, BENELEARN 2016. KortrijkResearch output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution
Open Access
File