Bayesian deep learning has always garnered substantial interest in the machine learning community.

A promising way to extract computational principles from these large datasets is to train data-constrained recurrent neural networks (dRNNs).

"subagents," one for each subproblem, and then combine their solutions to establish

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

The impossibility theorem of fairness is a foundational result in the algorithmic fairness literature. It states that outside of special cases, one cannot exactly and simultaneously satisfy all three common and intuitive definitions of fairness demographic parity, equalized odds, and predictive rate parity. This result has driven most works to focus on solutions for one or two of the metrics.