Search Results for author: Zeyang Yu

Found 5 papers, 3 papers with code

Reciprocal Adversarial Learning via Characteristic Functions

1 code implementation NeurIPS 2020 Shengxi Li, Zeyang Yu, Min Xiang, Danilo Mandic

For rigour, we first establish the physical meaning of the phase and amplitude in CF, and show that this provides a feasible way of balancing the accuracy and diversity of generation.

Solving general elliptical mixture models through an approximate Wasserstein manifold

1 code implementation9 Jun 2019 Shengxi Li, Zeyang Yu, Min Xiang, Danilo Mandic

To relieve this issue, we introduce an efficient optimisation method on a statistical manifold defined under an approximate Wasserstein distance, which allows for explicit metrics and computable operations, thus significantly stabilising and improving the EMM estimation.

Widely Linear Complex-valued Autoencoder: Dealing with Noncircularity in Generative-Discriminative Models

no code implementations5 Mar 2019 Zeyang Yu, Shengxi Li, Danilo Mandic

To resolve this issue, we design a new cost function, which is capable of controlling the balance between the phase and the amplitude contribution to the solution.

A universal framework for learning the elliptical mixture model

no code implementations21 May 2018 Shengxi Li, Zeyang Yu, Danilo Mandic

Mixture modelling using elliptical distributions promises enhanced robustness, flexibility and stability over the widely employed Gaussian mixture model (GMM).

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