3 papers
cs.LG2021
Covered Information Disentanglement: Model Transparency via Unbiased Permutation Importance
João Pereira, Erik S. G. Stroes, Aeilko H. Zwinderman +1
Model transparency is a prerequisite in many domains and an increasingly popular area in machine learning research. In the medical domain, for instance, unveiling the mechanisms be…
cs.LG2021
Interpretable Models via Pairwise permutations algorithm
Troy Maaslandand, João Pereira, Diogo Bastos +4
One of the most common pitfalls often found in high dimensional biological data sets are correlations between the features. This may lead to statistical and machine learning method…
cs.LG2019
Graph Space Embedding
João Pereira, Albert Groen, Erik Stroes +1
We propose the Graph Space Embedding (GSE), a technique that maps the input into a space where interactions are implicitly encoded, with little computations required. We provide th…