… To tackle this challenge, we select a Deep Learning architecture to perform unsupervised …
proposed architecture, in comparison to both well-known baselines and previous proposals. …

Deep learning (DL) systems have been gaining popularity in critical tasks such as credit
evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL …

… Deep learning is a powerful method for leveraging big data, but in many science and … ”
enough to ensure accuracy and reliability of deep learning models. What we may have instead is “…

… In figure 13, we show the comparison of error rate on each sub-population for neural network,
FAT and FLAT2. For the figure, it is clear that the proposed algorithm achieves decreases …

… Comparison of existing fair clustering methods and ours. … As a comparison, deep fair
clustering learns fair represen… [34] learn a subspace through deep learning and clustering …

… To this end, this paper introduces a differential privacy framework to train deep learning
models that satisfy several group fairness notions, including equalized odds, accuracy parity, …

… In this work a comprehensive analysis is carried out of how different expert systems (including
artificial intelligence, deep learning, and machine learning systems) are designed in order …

… build a fair and differentially private decentralised deep learning … accurate local models in a
fair and private manner by using … A succinct comparison among different deep learning frame…

… However, not all deep learning disciplines are able to use … Unfortunately, simple comparison
of BLEU scores is inferior to … of that addresses the fair reporting and comparison gap in …

… All comparative models share the same structures of the encoder network and classifier
as ours for a fair comparison. The results reported in this paper are averaged over three …