On Incorporating Inductive Biases into VAEs

25 June 2021

Papers citing "On Incorporating Inductive Biases into VAEs"

9 / 9 papers shown

Title
Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families Vaidotas Šimkus Michael U. Gutmann 40 2 0 05 Mar 2024
Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors Wasu Top Piriyakulkij Yingheng Wang Volodymyr Kuleshov DiffM 40 1 0 05 Jan 2024
Topological Obstructions and How to Avoid Them Babak Esmaeili Robin G. Walters Heiko Zimmermann Jan-Willem van de Meent AI4CE 24 3 0 12 Dec 2023
TC-VAE: Uncovering Out-of-Distribution Data Generative Factors Cristian Meo Anirudh Goyal Justin Dauwels DRL CoGe CML 27 1 0 08 Apr 2023
Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder F. Csikor B. Meszéna Bence Szabó Gergő Orbán BDL DRL 24 5 0 01 Jun 2022
Perspectives on Incorporating Expert Feedback into Model Updates Valerie Chen Umang Bhatt Hoda Heidari Adrian Weller Ameet Talwalkar 30 11 0 13 May 2022
A Sparsity-promoting Dictionary Model for Variational Autoencoders M. Sadeghi P. Magron 14 3 0 29 Mar 2022
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck Anirban Samaddar Sandeep Madireddy Prasanna Balaprakash Tapabrata Maiti Gustavo de los Campos Ian Fischer 24 1 0 04 Mar 2022
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks Yaniv Yacoby Weiwei Pan Finale Doshi-Velez CML DRL 27 25 0 14 Jul 2020