Publications

A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

Published in Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017

The Kalman variational auto-encoder is a framework for unsupervised learning of sequential data that disentangles two latent representations: an object’s representation, coming from a recognition model, and a latent state describing its dynamics. The recognition model is represented by a convolutional variational auto-encoder and the latent dynamics model as a linear Gaussian state space model (LGSSM).

Recommended citation: Marco Fraccaro, Simon Kamronn, Ulrich Paquet, Ole Winther. 2017. A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning. Advances in Neural Information Processing Systems 30. http://papers.nips.cc/paper/6951-a-disentangled-recognition-and-nonlinear-dynamics-model-for-unsupervised-learning.pdf