NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:2776
Title:Multivariate Triangular Quantile Maps for Novelty Detection

This paper proposes a novelty detection framework, using feature extraction via neural networks, density estimation via flows, and multiple gradient descent for optimization. The reviewers were unanimous in their vote to accept. Authors are encouraged to revise with respect to reviewer comments.