Big Data Deep Learning: Challenges and Perspectives

Deep learning is currently an extremely active research area in machine learning and pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language processing. With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. In this paper, we provide a brief overview of deep learning, and highlight current research efforts and the challenges to big data, as well as the future trends.

[1]  Jason Weston,et al.  Deep learning via semi-supervised embedding , 2008, ICML '08.

[2]  Antonio Torralba,et al.  Semi-Supervised Learning in Gigantic Image Collections , 2009, NIPS.

[3]  Thomas Hofmann,et al.  Greedy Layer-Wise Training of Deep Networks , 2007 .

[4]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[5]  Dong Yu,et al.  Tensor Deep Stacking Networks , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Robi Polikar,et al.  Incremental learning in nonstationary environments with controlled forgetting , 2009, 2009 International Joint Conference on Neural Networks.

[7]  J. Manyika Big data: The next frontier for innovation, competition, and productivity , 2011 .

[8]  Nicola Jones,et al.  Computer science: The learning machines , 2014, Nature.

[9]  Marc'Aurelio Ranzato,et al.  Semi-supervised learning of compact document representations with deep networks , 2008, ICML '08.

[10]  Mikhail Belkin,et al.  Semi-Supervised Learning Using Sparse Eigenfunction Bases , 2009, AAAI Fall Symposium: Manifold Learning and Its Applications.

[11]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[12]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[13]  James Martens,et al.  Deep learning via Hessian-free optimization , 2010, ICML.

[14]  Antonio Torralba,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 80 Million Tiny Images: a Large Dataset for Non-parametric Object and Scene Recognition , 2022 .

[15]  Yoram Singer,et al.  Pegasos: primal estimated sub-gradient solver for SVM , 2011, Math. Program..

[16]  Luca Maria Gambardella,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Flexible, High Performance Convolutional Neural Networks for Image Classification , 2022 .

[17]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

[18]  Patrice Y. Simard,et al.  Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[19]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[20]  David Saad,et al.  On-line learning with adaptive back-propagation in two-layer networks , 1997 .

[21]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Pierre Priouret,et al.  Adaptive Algorithms and Stochastic Approximations , 1990, Applications of Mathematics.

[23]  Dong Yu,et al.  Scalable stacking and learning for building deep architectures , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[24]  Geoffrey E. Hinton Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[25]  Rafael Martí,et al.  Multilayer neural networks: an experimental evaluation of on-line training methods , 2004, Comput. Oper. Res..

[26]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[27]  Dipti Srinivasan,et al.  Neural Networks for Continuous Online Learning and Control , 2006, IEEE Transactions on Neural Networks.

[28]  Avrim Blum,et al.  On-line Learning and the Metrical Task System Problem , 1997, COLT '97.

[29]  Alexander J. Smola,et al.  An architecture for parallel topic models , 2010, Proc. VLDB Endow..

[30]  Yoshua Bengio,et al.  Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.

[31]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[32]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[33]  Xue-wen Chen,et al.  Large-Scale Deep Belief Nets With MapReduce , 2014, IEEE Access.

[34]  Chee Peng Lim,et al.  Online pattern classification with multiple neural network systems: an experimental study , 2003, IEEE Trans. Syst. Man Cybern. Part C.

[35]  Rajat Raina,et al.  Large-scale deep unsupervised learning using graphics processors , 2009, ICML '09.

[36]  Saad,et al.  Exact solution for on-line learning in multilayer neural networks. , 1995, Physical review letters.

[37]  Robi Polikar,et al.  Incremental Learning of Concept Drift in Nonstationary Environments , 2011, IEEE Transactions on Neural Networks.

[38]  Hilbert J. Kappen,et al.  On-line learning processes in artificial neural networks , 1993 .

[39]  Dong Yu,et al.  Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[40]  Tao Wang,et al.  Deep learning with COTS HPC systems , 2013, ICML.

[41]  Roberto J. Bayardo,et al.  MapReduce and Its Application to Massively Parallel Learning of Decision Tree Ensembles , 2011 .

[42]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[43]  Yoav Freund,et al.  Game theory, on-line prediction and boosting , 1996, COLT '96.

[44]  Michael Biehl,et al.  On-line backpropagation in two-layered neural networks , 1995 .

[45]  R. Fergus,et al.  Learning invariant features through topographic filter maps , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[46]  L. Bottou Stochastic Gradient Learning in Neural Networks , 1991 .

[47]  Shai Shalev-Shwartz,et al.  Online Learning and Online Convex Optimization , 2012, Found. Trends Mach. Learn..

[48]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[49]  Berin Martini,et al.  Large-Scale FPGA-based Convolutional Networks , 2011 .

[50]  Luca Maria Gambardella,et al.  Deep, Big, Simple Neural Nets for Handwritten Digit Recognition , 2010, Neural Computation.

[51]  Olac Fuentes,et al.  Knowledge Transfer in Deep convolutional Neural Nets , 2007, Int. J. Artif. Intell. Tools.

[52]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.

[53]  Nitish Srivastava,et al.  Multimodal learning with deep Boltzmann machines , 2012, J. Mach. Learn. Res..

[54]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[55]  Motoaki Kawanabe,et al.  Machine Learning in Non-Stationary Environments - Introduction to Covariate Shift Adaptation , 2012, Adaptive computation and machine learning.

[56]  Klaus-Robert Müller,et al.  Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.

[57]  Yoshua Bengio,et al.  Unsupervised and Transfer Learning Challenge: a Deep Learning Approach , 2011, ICML Unsupervised and Transfer Learning.

[58]  G. Casella,et al.  Explaining the Gibbs Sampler , 1992 .

[59]  Pascal Vincent,et al.  Unsupervised and Transfer Learning under Uncertainty - From Object Detections to Scene Categorization , 2013, ICPRAM.

[60]  Yoshua Bengio,et al.  Deep Learning of Representations for Unsupervised and Transfer Learning , 2011, ICML Unsupervised and Transfer Learning.

[61]  Samy Bengio,et al.  Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks , 1999, NIPS.

[62]  Junhui Wang,et al.  Large Margin Semi-supervised Learning , 2007, J. Mach. Learn. Res..

[63]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

[64]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[65]  Philip M. Long,et al.  On-line learning of linear functions , 1991, STOC '91.

[66]  Bhaskar D. Rao,et al.  On-line learning algorithms for locally recurrent neural networks , 1999, IEEE Trans. Neural Networks.

[67]  Jimmy J. Lin,et al.  Large-scale machine learning at twitter , 2012, SIGMOD Conference.

[68]  Dong Yu,et al.  Conversational Speech Transcription Using Context-Dependent Deep Neural Networks , 2012, ICML.

[69]  Yoshua Bengio,et al.  Exploring Strategies for Training Deep Neural Networks , 2009, J. Mach. Learn. Res..

[70]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[71]  Jen-Tzung Chien,et al.  Nonstationary Source Separation Using Sequential and Variational Bayesian Learning , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[72]  Magnus Rattray,et al.  Globally optimal on-line learning rules for multi-layer neural networks , 1997, NIPS 1997.

[73]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[74]  Marc'Aurelio Ranzato,et al.  Large Scale Distributed Deep Networks , 2012, NIPS.

[75]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

[76]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.

[77]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[78]  Geoffrey E. Hinton,et al.  3D Object Recognition with Deep Belief Nets , 2009, NIPS.

[79]  Marc'Aurelio Ranzato,et al.  Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[80]  E. A. de Oliveira The Rosenblatt Bayesian Algorithm Learning in a Nonstationary Environment , 2007, IEEE Transactions on Neural Networks.

[81]  Peter L. Bartlett,et al.  Optimal Online Prediction in Adversarial Environments , 2010, Discovery Science.

[82]  Kunle Olukotun,et al.  Map-Reduce for Machine Learning on Multicore , 2006, NIPS.

[83]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[84]  Cesare Alippi,et al.  Just-in-Time Adaptive Classifiers—Part II: Designing the Classifier , 2008, IEEE Transactions on Neural Networks.

[85]  Leszek Rutkowski,et al.  Adaptive probabilistic neural networks for pattern classification in time-varying environment , 2004, IEEE Transactions on Neural Networks.

[86]  Geoffrey E. Hinton A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

[87]  Carme Torras,et al.  On-line learning with minimal degradation in feedforward networks , 1995, IEEE Trans. Neural Networks.

[88]  Graham W. Taylor,et al.  Adaptive deconvolutional networks for mid and high level feature learning , 2011, 2011 International Conference on Computer Vision.

[89]  Vincent Vanhoucke,et al.  Improving the speed of neural networks on CPUs , 2011 .

[90]  Marc'Aurelio Ranzato,et al.  Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.

[91]  Christian Viard-Gaudin,et al.  A Convolutional Neural Network Approach for Objective Video Quality Assessment , 2006, IEEE Transactions on Neural Networks.

[92]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[93]  Cesare Alippi,et al.  Just-in-Time Adaptive Classifiers—Part I: Detecting Nonstationary Changes , 2008, IEEE Transactions on Neural Networks.