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Gaussian RBM

A Gaussian Restricted Boltzmann Machine is an energy-based generative neural network designed to model continuous, real-valued input data. Unlike standard Restricted Boltzmann Machines that use binary visible units, a Gaussian Restricted Boltzmann Machine equips visible units with continuous variables governed by Gaussian distributions while typically maintaining stochastic binary hidden units. The architecture employs a quadratic energy function to parameterize the mean and variance of the observed data, enabling the network to capture complex, continuous probability distributions found in domains such as computer vision and audio processing. These models are commonly applied for feature extraction, continuous data generation, and as the bottom layer in deeper hierarchical models such as deep belief networks and deep Boltzmann machines.

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Multimodal learning with deep Boltzmann machines

Multimodal learning with deep Boltzmann machines

Nitish Srivastava, Ruslan Salakhutdinov

OrganizationsUniversity of Toronto

Why you should read this

Proposes a Multimodal Deep Boltzmann Machine that learns a joint generative model across disparate modalities like images and text, enabling effective classification, cross-modal retrieval, and the reconstruction of missing inputs.

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 this representation even when some modalities are absent by sampling from the conditional distribution over them and filling them in. Our experimental results on bi-modal data consisting of images and text show that the Multimodal DBM can learn a good generative model of the joint space of image and text inputs that is useful for information retrieval from both unimodal and multimodal queries. We further demonstrate that this model significantly outperforms SVMs and LDA on discriminative tasks. Finally, we compare our model to other deep learning methods, including autoencoders and deep belief networks, and show that it achieves noticeable gains.

Added

2026-09-18