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Multimodal Learning with Deep Boltzmann Machines
2012
Neural Information Processing Systems
A Deep Boltzmann Machine is described for learning a generative model of data that consists of multiple and diverse input modalities. The model can be used to extract a unified representation that fuses modalities together. We find that this representation is useful for classification and information retrieval tasks. The model works by learning a probability density over the space of multimodal inputs. It uses states of latent variables as representations of the input. The model can extract
dblp:conf/nips/SrivastavaS12
fatcat:dnhlapisb5fpvn322psjhews6e