| DOI | Trouver le DOI : https://doi.org/10.1109/IJCNN.2018.8489413 |
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| Auteur | Rechercher : Li, Yifeng1; Rechercher : Zhu, Xiaodan |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 2018 International Joint Conference on Neural Networks (IJCNN), July 7-13, 2018, Rio de Janeiro, Brazil |
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| Sujet | deep learning; generative model; exponential family; restricted Boltzmann machine; annealed importance sampling |
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| Résumé | In this paper, we investigate restricted Boltzmann machines (RBMs) from the exponential family perspective, en-abling the visible units to follow any suitable distributions from the exponential family. We derive a unified view to compute the free energy function for exponential family RBMs (exp-RBMs). Based on that, annealed important sampling (AIS) is generalized to the entire exponential family, allowing for estimating the log-partition function and log-likelihood. Our experiments on a document processing task demonstrate that the generalized free energy functions and AIS estimation perform well in helping capture useful knowledge from the data; the estimated log-partition functions are stable. The appropriate instances of exp-RBMs can generate novel and meaningful samples and can be applied to classification tasks. |
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| Date de publication | 2018-10-15 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 3cf762e2-b2b8-4072-b7b0-fe6c993f8c9f |
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| Enregistrement créé | 2019-04-12 |
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| Enregistrement modifié | 2020-03-16 |
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