Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders

30 September 2022

Papers citing "Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders"

8 / 8 papers shown

Title
Efficient Mixture Learning in Black-Box Variational Inference A. Hotti Oskar Kviman Ricky Molén Victor Elvira Jens Lagergren 41 1 0 11 Jun 2024
Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families Vaidotas Šimkus Michael U. Gutmann 40 2 0 05 Mar 2024
Indirectly Parameterized Concrete Autoencoders Alfred Nilsson Klas Wijk Sai Bharath Chandra Gutha Erik Englesson A. Hotti Carlo Saccardi Oskar Kviman Jens Lagergren Ricardo Vinuesa Hossein Azizpour 38 1 0 01 Mar 2024
InVA: Integrative Variational Autoencoder for Harmonization of Multi-modal Neuroimaging Data Bowen Lei Rajarshi Guhaniyogi Krishnendu Chandra Aaron Scheffler Bani Mallick 15 0 0 05 Feb 2024
Improved Variational Bayesian Phylogenetic Inference using Mixtures Oskar Kviman Ricky Molén Jens Lagergren 20 5 0 02 Oct 2023
Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations Oskar Kviman Harald Melin Hazal Koptagel Victor Elvira J. Lagergren DRL 68 17 0 22 Feb 2022
Pixel Recurrent Neural Networks Aaron van den Oord Nal Kalchbrenner Koray Kavukcuoglu SSeg GAN 233 2,547 0 25 Jan 2016
Gradient Importance Sampling Ingmar Schuster 30 25 0 21 Jul 2015