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Found 4 papers, 2 papers with code
LUCID-GAN: Conditional Generative Models to Locate Unfairness
Most group fairness notions detect unethical biases by computing statistical parity metrics on a model's output.
Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies
We here present a stepping stone towards a deeper understanding of convolutional neural networks (CNNs) in the form of a theory of learning in linear CNNs.
LUCID: Exposing Algorithmic Bias through Inverse Design
The results show that by shifting the focus towards equality of treatment and looking into the algorithm's internal workings, the canonical sets are a valuable addition to the toolbox of algorithmic fairness evaluation.
Data driven design of optical resonators
In this paradigm, one starts from the desired optical response of a material and then determines the design parameters that are needed to obtain this optical response.
