Exploring the Latent Space of Autoencoders with Interventional Assays

Exploring the Latent Space of Autoencoders with Interventional Assays

30 June 2021

Stefan Bauer

Bernhard Schölkopf

Papers citing "Exploring the Latent Space of Autoencoders with Interventional Assays"

8 / 8 papers shown

Title
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models Ruta Binkyte Ivaxi Sheth Zhijing Jin Mohammad Havaei Bernhard Schölkopf Mario Fritz 81 0 0 28 Feb 2025
Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance Zexin Hu Kun Hu Clinton Mo Lei Pan Zhiyong Wang DiffM 13 2 0 31 Aug 2023
Trustworthy AI: From Principles to Practices Bo-wen Li Peng Qi Bo Liu Shuai Di Jingen Liu Jiquan Pei Jinfeng Yi Bowen Zhou 117 354 0 04 Oct 2021
Variational Autoencoders with Riemannian Brownian Motion Priors Dimitris Kalatzis David Eklund Georgios Arvanitidis Søren Hauberg BDL DRL 53 49 0 12 Feb 2020
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Jan Stühmer Richard E. Turner Sebastian Nowozin DRL BDL CoGe 80 25 0 05 Sep 2019
Understanding Autoencoders with Information Theoretic Concepts Shujian Yu José C. Príncipe AI4CE 41 132 0 30 Mar 2018
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 234 1,809 0 25 Nov 2016
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 231 3,230 0 24 Nov 2016