On Disentanglement in Gaussian Process Variational Autoencoders

10 February 2021

Papers citing "On Disentanglement in Gaussian Process Variational Autoencoders"

21 / 21 papers shown

Title
Scalable Gaussian Process Variational Autoencoders Metod Jazbec Matthew Ashman Vincent Fortuin Michael Pearce Stephan Mandt Gunnar Rätsch DRL BDL 61 29 0 26 Oct 2020
Sparse Gaussian Process Variational Autoencoders Matthew Ashman Jonathan So Will Tebbutt Vincent Fortuin Michael Pearce Richard Turner 104 35 0 20 Oct 2020
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 242 320 0 07 Feb 2020
On the Fairness of Disentangled Representations Francesco Locatello G. Abbati Tom Rainforth Stefan Bauer Bernhard Schölkopf Olivier Bachem FaML DRL 73 227 0 31 May 2019
Are Disentangled Representations Helpful for Abstract Visual Reasoning? Sjoerd van Steenkiste Francesco Locatello Jürgen Schmidhuber Olivier Bachem 85 210 0 29 May 2019
Disentangling Factors of Variation Using Few Labels Francesco Locatello Michael Tschannen Stefan Bauer Gunnar Rätsch Bernhard Schölkopf Olivier Bachem DRL CML CoGe 94 124 0 03 May 2019
Recent Advances in Autoencoder-Based Representation Learning Michael Tschannen Olivier Bachem Mario Lucic OOD SSL DRL 75 446 0 12 Dec 2018
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations Francesco Locatello Stefan Bauer Mario Lucic Gunnar Rätsch Sylvain Gelly Bernhard Schölkopf Olivier Bachem OOD 134 1,473 0 29 Nov 2018
Gaussian Process Prior Variational Autoencoders F. P. Casale Adrian Dalca Luca Saglietti Jennifer Listgarten Nicolò Fusi BDL CML 61 138 0 28 Oct 2018
Disentangled Sequential Autoencoder Yingzhen Li Stephan Mandt CoGe 71 271 0 08 Mar 2018
Disentangling by Factorising Hyunjik Kim A. Mnih CoGe OOD 64 1,356 0 16 Feb 2018
Learning Deep Disentangled Embeddings with the F-Statistic Loss Karl Ridgeway Michael C. Mozer FedML DRL CoGe 72 218 0 14 Feb 2018
Isolating Sources of Disentanglement in Variational Autoencoders T. Chen Xuechen Li Roger C. Grosse David Duvenaud DRL 61 447 0 14 Feb 2018
Building machines that adapt and compute like brains Brenden M. Lake J. Tenenbaum AI4CE FedML NAI AILaw 322 887 0 11 Nov 2017
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data Wei-Ning Hsu Yu Zhang James R. Glass BDL SSL 80 353 0 22 Sep 2017
Dynamic Word Embeddings Robert Bamler Stephan Mandt BDL 216 232 0 27 Feb 2017
A Survey of Inductive Biases for Factorial Representation-Learning Karl Ridgeway DRL CML 68 76 0 15 Dec 2016
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever Pieter Abbeel GAN 161 4,238 0 12 Jun 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 298 4,812 0 04 Jan 2016
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 455 16,923 0 20 Dec 2013
Representation Learning: A Review and New Perspectives Yoshua Bengio Aaron Courville Pascal Vincent OOD SSL 278 12,458 0 24 Jun 2012