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Found 6 papers, 1 papers with code
Invariant Representations with Stochastically Quantized Neural Networks
In this paper, we propose a methodology for direct computation of the mutual information between a neural layer and a sensitive attribute.
Fair Interpretable Representation Learning with Correction Vectors
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 which is independent of sensitive information.
Fair Interpretable Learning via Correction Vectors
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 which is independent of sensitive information.
Ranking Creative Language Characteristics in Small Data Scenarios
We therefore adapt the DirectRanker to provide a new deep model for ranking creative language with small data.
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance
We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture.
