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. Multimodal learning involves relating information from multiple sources.

Multimodal deep learning
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Deep networks have been successfully applied to unsupervised feature learning for single modalities (eg, 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

Learning representations for multimodal data with deep belief nets
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Abstract 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

Damage Identification in Social Media Posts using Multimodal Deep Learning .
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Social media has recently become a digital lifeline used to relay information and locate survivors in disaster situations. Currently, officials and volunteers scour social media for any valuable information; however, this approach is implausible as millions of posts are shared

Multimodal medical image fusion based on deep learning neural network for clinical treatment analysis
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Multimodal medical image fusion technique is one of the most significant and useful disease investigative techniques by deriving the complementary information from different multimodality medical images. This research paper, proposed an efficient multimodal

Multimodal biometrics recognition from facial video via deep learning
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Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a

Multimodal deep learning
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Deep learning is a new area of machine learning research that imitates the way the human brain works. It has a great number of successful applications in speech recognition, image classification, and natural language processing. It is a particular approach to build and train

Success prediction on crowdfunding with multimodal deep learning
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We consider the problem of project success prediction on crowdfunding platforms. Despite the information in a project profile can be of different modalities such as text, images, and metadata, most existing prediction approaches leverage only the text dominated modality

Deep learning in medical image analysis and multimodal learning for clinical decision support
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The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general

Multimodal Sentiment Analysis in Social Media using Deep Learning with Convolutional Neural Networks
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Due to the rapid growth of internet and proliferation of smartphones and tablets, socialmedia platforms like Facebook and YouTube have risen to great relevance. People share all sorts of contents in such platforms, leading to huge amount of data requiring processing

A Deep Reinforcement Learning Based Multimodal Coaching Model (DCM) for Slot Filling in Spoken Language Understanding (SLU).
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In this paper, a deep reinforcement learning (DRL) based multimodal coaching model (DCM) for slot filling task in SLU is proposed. The DCM takes advantage of a DRL based model as a coach of the system to learn the wrong labeled slots with/without users

Learning Reliable and Scalable Representations Using Multimodal Multitask Deep Learning
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Fifties-in 5 years robots would be everywhere. Sixties-in 10 years robots would be everywhere. Seventies-in 20 years robots would be everywhere. Eighties-in 40 years robots would be everywhere.-Marvin Minsky Those were the words from one of the pioneers of AI

Interpretability in Multimodal Deep Learning
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We focus on the processing of heterogeneous sources of information with the aim of determining which modalities or their combinations play an important role in prediction. The interaction of high level features of combinations is modelled through tensor products. We

MIMETIC: Mobile Encrypted Traffic Classification using Multimodal Deep Learning
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Multimodal Deep Learning
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Precision medicine is defined as the prevention and treatment strategies that take individual variability into account . Implementing precision medicine in a clinical setting requires seamless integration of data from clinical evaluations and bio medical investigations with

Cross-Modal Retrieval using Random Multimodal Deep Learning
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In multimedia community, cross modal similarity search based hashing received extensive attention because of the effectiveness and efficiency of query. This research work contributes large scale dataset for weakly managed cross-media recovery, named

A Tutorial Survey on Multimodal Deep Learning
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Different data modalities typically reflect distinct properties of the underlying object or process. Instead of processing modalities independently, it is highly desirable to exploit this complementarity to obtain more representative models. Multimodal learning is closely

Deep Learning Models for Multimodal Sensing and Processing
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Multimodal sensing and processing have shown promising results in detection, recognition and identification in various applications, such as human-computer interaction, surveillance, medical diagnosis, biometrics, etc. There are many ways to generate multiple modalities;

Implications of Multimodal Deep Learning for Textual and Visual Data
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While deep learning has been successful in a wide range of tasks in different fields, most of the current neural network based systems are still learning in merely one source of knowledge such as text, image or wave. In this work, we propose a novel method that

Towards Natural Language Understanding using Multimodal Deep Learning
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This thesis describes how multimodal sensor data from a 3D sensor and microphone array can be processed with deep neural networks such that its fusion, the trained neural network, is a) more robust to noise, b) outperforms unimodal recognition and c) enhances unimodal

Detection of First-degree Atrioventricular Block on Variable-length Electrocardiogram via a Multimodal Deep Learning Method
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Automatic detection of first-degree atrioventricular block (I-AVB) from electrocardiogram (ECG) is of great importance in prevention of more severe cardiac diseases. I-AVB is characterized by a prolonged PR interval. However, due to various artifacts and diversity of


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