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sparse word count vectors

Sparse word count vectors are numerical representations of text documents in which each dimension corresponds to a distinct word from a fixed vocabulary, and each entry reflects the frequency of that word in the document. Because any individual document typically contains only a small fraction of all possible words in the vocabulary, the vast majority of the vector elements are zeros, making the data structure sparse. Frequently employed in bag-of-words models, information retrieval, and machine learning pipelines, these vectors translate unstructured text into fixed-length quantitative features, capturing term frequencies while disregarding grammatical structure and word order.

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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