11 citations · 27 across the 7 of their papers we have counts for
5 papers · 1 filter
Neural RELAGGS
Lukas Pensel, Stefan Kramer
Multi-relational databases are the basis of most consolidated data collections in science and industry today. Most learning and mining algorithms, however, require data to be repre…
A Fair Experimental Comparison of Neural Network Architectures for Latent Representations of Multi-Omics for Drug Response Prediction
Tony Hauptmann, Stefan Kramer
Recent years have seen a surge of novel neural network architectures for the integration of multi-omics data for prediction. Most of the architectures include either encoders alone…
Invariant Representations with Stochastically Quantized Neural Networks
Mattia Cerrato, Marius Köppel, Roberto Esposito +1
Representation learning algorithms offer the opportunity to learn invariant representations of the input data with regard to nuisance factors. Many authors have leveraged such stra…
Fair Interpretable Representation Learning with Correction Vectors
Mattia Cerrato, Alesia Vallenas Coronel, Marius Köppel +3
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector whi…
Fair Interpretable Learning via Correction Vectors
Mattia Cerrato, Marius Köppel, Alexander Segner +1
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector whi…