Deep learning applications and challenges in big data analytics

Maryam M. Najafabadi; Flavio Villanustre; Taghi M. Khoshgoftaar; Naeem Seliya; Randall Wald; Edin Muharemagic

doi:https://doi.org/10.1186/s40537-014-0007-7

Deep learning applications and challenges in big data analytics

2015 · Journal Of Big Data · 2,610 citations · 6 from inside this corpus

Maryam M. Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald low, Edin Muharemagic low

Abstract Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simpler abstractions formulated in the preceding level in the hierarchy. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. We also investigate some aspects of Deep Learning research that need further exploration to incorporate specific challenges introduced by Big Data Analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. We conclude by presenting insights into relevant future works by posing some questions, including defining data sampling criteria, domain adaptation modeling, defining criteria for obtaining useful data abstractions, improving semantic indexing, semi-supervised learning, and active learning.

19 of 19 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

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A survey of deep neural network architectures and their applications→Deep learning applications and challenges in big data analyticsLegacy edge — source detail unavailable
Survey on deep learning with class imbalance→Deep learning applications and challenges in big data analyticsLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Deep learning applications and challenges in big data analyticsLegacy edge — source detail unavailable
Review of Deep Learning Algorithms and Architectures→Deep learning applications and challenges in big data analyticsLegacy edge — source detail unavailable
A State-of-the-Art Survey on Deep Learning Theory and Architectures→Deep learning applications and challenges in big data analyticsLegacy edge — source detail unavailable
A Survey on Deep Learning→Deep learning applications and challenges in big data analyticsLegacy edge — source detail unavailable
Deep learning applications and challenges in big data analytics→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
Deep learning applications and challenges in big data analytics→Efficient Estimation of Word Representations in Vector SpaceLegacy edge — source detail unavailable
Deep learning applications and challenges in big data analytics→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
Deep learning applications and challenges in big data analytics→Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…Legacy edge — source detail unavailable
Deep learning applications and challenges in big data analytics→Term-weighting approaches in automatic text retrievalLegacy edge — source detail unavailable
Deep learning applications and challenges in big data analytics→Improving neural networks by preventing co-adaptation of feature detectorsLegacy edge — source detail unavailable
Deep learning applications and challenges in big data analytics→Learning Deep Architectures for AILegacy edge — source detail unavailable
Deep learning applications and challenges in big data analytics→Training Products of Experts by Minimizing Contrastive DivergenceLegacy edge — source detail unavailable
Deep learning applications and challenges in big data analytics→A few useful things to know about machine learningLegacy edge — source detail unavailable
Deep learning applications and challenges in big data analytics→Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech RecognitionLegacy edge — source detail unavailable
Deep learning applications and challenges in big data analytics→Acoustic Modeling Using Deep Belief NetworksLegacy edge — source detail unavailable
Deep learning applications and challenges in big data analytics→Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning ApproachLegacy edge — source detail unavailable
Deep learning applications and challenges in big data analytics→Exploiting Similarities among Languages for Machine TranslationLegacy edge — source detail unavailable
Improving neural networks by preventing co-adaptation of feature detectors→Acoustic Modeling Using Deep Belief NetworksLegacy edge — source detail unavailable
Improving neural networks by preventing co-adaptation of feature detectors→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
Improving neural networks by preventing co-adaptation of feature detectors→Training Products of Experts by Minimizing Contrastive DivergenceLegacy edge — source detail unavailable
Improving neural networks by preventing co-adaptation of feature detectors→Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech RecognitionLegacy edge — source detail unavailable
Acoustic Modeling Using Deep Belief Networks→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
Acoustic Modeling Using Deep Belief Networks→Training Products of Experts by Minimizing Contrastive DivergenceLegacy edge — source detail unavailable
Acoustic Modeling Using Deep Belief Networks→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
Acoustic Modeling Using Deep Belief Networks→Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech RecognitionLegacy edge — source detail unavailable
Learning Deep Architectures for AI→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
Learning Deep Architectures for AI→Training Products of Experts by Minimizing Contrastive DivergenceLegacy edge — source detail unavailable
Learning Deep Architectures for AI→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
Reducing the Dimensionality of Data with Neural Networks→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
A Fast Learning Algorithm for Deep Belief Nets→Training Products of Experts by Minimizing Contrastive DivergenceLegacy edge — source detail unavailable
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition→Acoustic Modeling Using Deep Belief NetworksLegacy edge — source detail unavailable
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition→Learning Deep Architectures for AILegacy edge — source detail unavailable
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition→Training Products of Experts by Minimizing Contrastive DivergenceLegacy edge — source detail unavailable
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…→Acoustic Modeling Using Deep Belief NetworksLegacy edge — source detail unavailable
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…→Training Products of Experts by Minimizing Contrastive DivergenceLegacy edge — source detail unavailable
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…→Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech RecognitionLegacy edge — source detail unavailable
A few useful things to know about machine learning→Learning Deep Architectures for AILegacy edge — source detail unavailable
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach→Learning Deep Architectures for AILegacy edge — source detail unavailable
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
A survey of deep neural network architectures and their applications→Acoustic Modeling Using Deep Belief NetworksLegacy edge — source detail unavailable
A survey of deep neural network architectures and their applications→Learning Deep Architectures for AILegacy edge — source detail unavailable
A survey of deep neural network architectures and their applications→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
A survey of deep neural network architectures and their applications→Training Products of Experts by Minimizing Contrastive DivergenceLegacy edge — source detail unavailable
A survey of deep neural network architectures and their applications→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
A survey of deep neural network architectures and their applications→Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech RecognitionLegacy edge — source detail unavailable
A survey of deep neural network architectures and their applications→Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…Legacy edge — source detail unavailable
A survey of deep neural network architectures and their applications→Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning ApproachLegacy edge — source detail unavailable
A Survey on Deep Learning→Learning Deep Architectures for AILegacy edge — source detail unavailable
A Survey on Deep Learning→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
A Survey on Deep Learning→Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech RecognitionLegacy edge — source detail unavailable
A Survey on Deep Learning→Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…Legacy edge — source detail unavailable
A State-of-the-Art Survey on Deep Learning Theory and Architectures→Acoustic Modeling Using Deep Belief NetworksLegacy edge — source detail unavailable
A State-of-the-Art Survey on Deep Learning Theory and Architectures→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
A State-of-the-Art Survey on Deep Learning Theory and Architectures→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
Survey on deep learning with class imbalance→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
Review of Deep Learning Algorithms and Architectures→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
Review of Deep Learning Algorithms and Architectures→A Survey on Deep LearningLegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Learning Deep Architectures for AILegacy edge — source detail unavailable
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…Legacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Reducing the Dimensionality of Data with Neural Networks	2006	21,247
Efficient Estimation of Word Representations in Vector Space	2013	18,186
A Fast Learning Algorithm for Deep Belief Nets	2006	16,540
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…	2012	10,399
Term-weighting approaches in automatic text retrieval	1988	9,714
Improving neural networks by preventing co-adaptation of feature detectors	2012	6,653
Learning Deep Architectures for AI	2009	5,077
Training Products of Experts by Minimizing Contrastive Divergence	2002	5,032
A few useful things to know about machine learning	2012	3,338
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition	2011	3,089
Acoustic Modeling Using Deep Belief Networks	2011	1,752
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach	2011	1,562
Exploiting Similarities among Languages for Machine Translation	2013	1,442

What cites it, inside the corpus

Paper	Year	Cited
A survey of deep neural network architectures and their applications	2016	3,285
Survey on deep learning with class imbalance	2019	2,971
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey	2018	2,090
Review of Deep Learning Algorithms and Architectures	2019	1,878
A State-of-the-Art Survey on Deep Learning Theory and Architectures	2019	1,619
A Survey on Deep Learning	2018	1,398

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Data Stream Mining Techniques	Computer Science
Time Series Analysis and Forecasting	Computer Science
Data Quality and Management	Decision Sciences

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