Variational autoencoders in the presence of low-dimensional data:
landscape and implicit bias

Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias

13 December 2021

Frederic Koehler

Andrej Risteski

Papers citing "Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias"

9 / 9 papers shown

Title
Unpicking Data at the Seams: Understanding Disentanglement in VAEs Carl Allen CML CoGe 25 0 0 29 Oct 2024
Beta-Sigma VAE: Separating beta and decoder variance in Gaussian variational autoencoder Seunghwan Kim Seungkyu Lee DRL 18 0 0 14 Sep 2024
On the Convergence Analysis of Over-Parameterized Variational Autoencoders: A Neural Tangent Kernel Perspective Li Wang Wei Huang DRL 11 0 0 09 Sep 2024
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data Saptarshi Chakraborty Peter L. Bartlett 14 1 0 24 Feb 2024
Objective-Agnostic Enhancement of Molecule Properties via Multi-Stage VAE Chenghui Zhou Barnabás Póczós 15 0 0 24 Aug 2023
Improving Molecule Properties Through 2-Stage VAE Chenghui Zhou Barnabás Póczós DRL 10 1 0 06 Dec 2022
Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games Druv Pai Michael Psenka Chih-Yuan Chiu Manxi Wu Edgar Dobriban Y. Ma 17 6 0 18 Jun 2022
Exact Solutions of a Deep Linear Network Liu Ziyin Botao Li Xiangmin Meng ODL 16 21 0 10 Feb 2022
Stochastic Neural Networks with Infinite Width are Deterministic Liu Ziyin Hanlin Zhang Xiangming Meng Yuting Lu Eric P. Xing Masakuni Ueda 14 3 0 30 Jan 2022