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Multimodal learning

The information in real world usually comes as different modalities. For example, images are usually associated with tags and text explanations… Expand
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Papers overview

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Highly Cited
2017
Highly Cited
2017
We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from… Expand
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2017
2017
In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful… Expand
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Highly Cited
2016
Highly Cited
2016
Hashing or binary code learning has been recognized to accomplish efficient near neighbor search, and has thus attracted broad… Expand
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Highly Cited
2015
Highly Cited
2015
In this paper, we present methods in deep multimodal learning for fusing speech and visual modalities for Audio-Visual Automatic… Expand
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Highly Cited
2014
Highly Cited
2014
Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns… Expand
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Highly Cited
2013
Highly Cited
2013
New high-frequency data collection technologies and machine learning analysis techniques could offer new insights into learning… Expand
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Highly Cited
2012
Highly Cited
2012
Data often consists of multiple diverse modalities. For example, images are tagged with textual information and videos are… Expand
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Highly Cited
2011
Highly Cited
2011
Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio… Expand
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Highly Cited
2010
Highly Cited
2010
[Abstract]: The innovative use of educational technologies provides higher education institutions valuable opportunities for… Expand
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Highly Cited
2008
Highly Cited
2008
Foreword This report is the third in a series that addresses " what the research says, " as Education's ethos states that… Expand
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