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Stopping criteria in contrastive divergence: Alternatives to the reconstruction error
dc.contributor.author | Buchaca Prats, David |
dc.contributor.author | Romero Merino, Enrique |
dc.contributor.author | Mazzanti Castrillejo, Fernando Pablo |
dc.contributor.author | Delgado Pin, Jordi |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Física i Enginyeria Nuclear |
dc.date.accessioned | 2015-03-09T12:28:53Z |
dc.date.available | 2015-03-09T12:28:53Z |
dc.date.created | 2014 |
dc.date.issued | 2014 |
dc.identifier.citation | Buchaca, D. [et al.]. Stopping criteria in contrastive divergence: Alternatives to the reconstruction error. A: International Conference on Learning Representations. "International Conference on Learning Representations 2014: workshop proceedings". Banff: 2014. |
dc.identifier.uri | http://hdl.handle.net/2117/26625 |
dc.description.abstract | Restricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions. RBMs are often trained using the Contrastive Divergence learning algorithm (CD), an approximation to the gradient of the data log-likelihood. A simple reconstruction error is often used to decide whether the approximation provided by the CD algorithm is good enough, though several authors (Schulz et al., 2010; Fischer & Igel, 2010) have raised doubts concerning the feasibility of this procedure. However, not many alternatives to the reconstruction error have been used in the literature. In this manuscript we investigate simple alternatives to the reconstruction error in order to detect as soon as possible the decrease in the log-likelihood during learning. |
dc.format.extent | 8 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | Machine learning |
dc.subject.lcsh | Computer algorithms |
dc.subject.other | Restricted Boltzmann Machines |
dc.subject.other | RBMs: Contrastive divergence learning algorithm |
dc.title | Stopping criteria in contrastive divergence: Alternatives to the reconstruction error |
dc.type | Conference report |
dc.subject.lemac | Aprenentatge automàtic |
dc.subject.lemac | Algorismes computacionals |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.contributor.group | Universitat Politècnica de Catalunya. SIMCON - First-principles approaches to condensed matter physics: quantum effects and complexity |
dc.contributor.group | Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://sites.google.com/site/representationlearning2014/workshop-proceedings |
dc.rights.access | Open Access |
local.identifier.drac | 15451088 |
dc.description.version | Postprint (published version) |
local.citation.author | Buchaca, D.; Romero, E.; Mazzanti, F.; Delgado, J. |
local.citation.contributor | International Conference on Learning Representations |
local.citation.pubplace | Banff |
local.citation.publicationName | International Conference on Learning Representations 2014: workshop proceedings |