Editorial: Deep learning for multimodal brain data processing and analysis

Deep Learning Models for Multimodal Sensing and Processing

Deep learning: from speech recognition to language and multimodal processing

APSIPA Transactions on Signal and Information Processing / Volume 5 / 2016 / e1 DOI: 10.1017/atsip.2015.22, Published online: 19 January 2016 Link to this article: http://journals.cambridge.org/abstract_S2048770315000220 How to cite this article: Li Deng (2016). Deep learning: from speech recognition to language and multimodal processing. APSIPA Transactions on Signal and Information Processing...

Learning Representations for Multimodal Data with Deep Belief Nets

We propose a Deep Belief Network architecture for learning a joint representation of multimodal data. The model defines a probability distribution over the space of multimodal inputs and allows sampling from the conditional distributions over each data modality. This makes it possible for the model to create a multimodal representation even when some data modalities are missing. Our experimenta...

Multimodal Deep Learning Library

The Neural Network is a directed graph consists of multiple layers of neurons, which is also referred to as units. In general there is no connection between units of the same layer and there are only connections between adjacent layers. The first layer is the input and is referred to as visible layer v. Above the visible layer there are multiple hidden layers {h1, h2, ..., hn}. And the output o...

Multimodal Deep Learning

Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep networks that learn features to address these tasks. In particular, we demonstra...