Temporal Autoencoding Restricted Boltzmann Machine
Machine Learning
2012-11-01 v1 Artificial Intelligence
Machine Learning
Abstract
Much work has been done refining and characterizing the receptive fields learned by deep learning algorithms. A lot of this work has focused on the development of Gabor-like filters learned when enforcing sparsity constraints on a natural image dataset. Little work however has investigated how these filters might expand to the temporal domain, namely through training on natural movies. Here we investigate exactly this problem in established temporal deep learning algorithms as well as a new learning paradigm suggested here, the Temporal Autoencoding Restricted Boltzmann Machine (TARBM).
Cite
@article{arxiv.1210.8353,
title = {Temporal Autoencoding Restricted Boltzmann Machine},
author = {Chris Häusler and Alex Susemihl},
journal= {arXiv preprint arXiv:1210.8353},
year = {2012}
}