FRMDN: Flow-based Recurrent Mixture Density Network

Seyedeh Fatemeh Razavi, Reshad Hosseini, Tina Behzad

Abstract: Highlights•Two aspects of RMDNs have been explored for efficient density estimation.•A normalizing flow is employed to increase the flexibility of RMDNs.•A parameter-sharing approach for GMM is applied that decomposes the precision matrix.•Shared parameters among components are obtained directly or by a neural network.•Using normalizing flow and decomposition in RMDN leads to suitable likelihood scores.