English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Multimodal learning with deep Boltzmann machines (DBMs) is an generativeapproach to fuse multimodal inputs, and can learn the shared representation viaContrastive Divergence (CD) for classification and information retrieval tasks.However, it is a 2-fan DBM model, and cannot effectively handle multipleprediction tasks. Moreover, this model cannot recover the hiddenrepresentations well by sampling from the conditional distribution when morethan one modalities are missing. In this paper, we propose a K-fan deepstructure model, which can handle the multi-input and muti-output learningproblems effectively. In particular, the deep structure has K-branch fordifferent inputs where each branch can be composed of a multi-layer deep model,and a shared representation is learned in an discriminative manner to tacklemultimodal tasks. Given the deep structure, we propose two objective functionsto handle two multi-input and multi-output tasks: joint visual restoration andlabeling, and the multi-view multi-calss object recognition tasks. To estimatethe model parameters, we initialize the deep model parameters with CD tomaximize the joint distribution, and then we use backpropagation to update themodel according to specific objective function. The experimental resultsdemonstrate that the model can effectively leverages multi-source informationand predict multiple tasks well over competitive baselines.

Generalized K-fan Multimodal Deep Model with Shared Representations