research-article

A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation Learning

Authors:

Mohanad Abukmeil,

Stefano Ferrari,

Angelo Genovese,

Vincenzo Piuri,

Fabio ScottiAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 5

Article No.: 99, Pages 1 - 40

https://doi.org/10.1145/3450963

Published: 09 July 2021 Publication History

Abstract

For more than a century, the methods for data representation and the exploration of the intrinsic structures of data have developed remarkably and consist of supervised and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high and the data can come in messy, incomplete, unlabeled, or corrupted forms. Consequently, discovering the hidden structure buried inside such data becomes highly challenging. From this perspective, exploratory data analysis plays a substantial role in learning the hidden structures that encompass the significant features of the data in an ordered manner by extracting patterns and testing hypotheses to identify anomalies. Unsupervised generative learning models are a class of machine learning models characterized by their potential to reduce the dimensionality, discover the exploratory factors, and learn representations without any predefined labels; moreover, such models can generate the data from the reduced factors’ domain. The beginner researchers can find in this survey the recent unsupervised generative learning models for the purpose of data exploration and learning representations; specifically, this article covers three families of methods based on their usage in the era of big data: blind source separation, manifold learning, and neural networks, from shallow to deep architectures.

Supplementary Material

a99-abukmeil-supp.pdf (abukmeil.zip)

Supplemental movie, appendix, image and software files for, A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation Learning

Download
853.03 KB

References

[1]

M. A. Abukmeil, S. Ferrari, A. Genovese, V. Piuri, and F. Scotti. 2020. On approximating the non-negative rank: Applications to image reduction. In Proc. of CIVEMSA.

[2]

Mohanad A. M. Abukmeil, Hatem Elaydi, and Mohammed Alhanjouri. 2015. Palmprint recognition via bandlet, ridgelet, wavelet and neural network. Journal of Computer Sciences and Applications 3, 2 (2015), 23–28.

[3]

David H. Ackley, Geoffrey E. Hinton, and Terrence J. Sejnowski. 1985. A learning algorithm for Boltzmann machines. Cognitive Science 9, 1 (1985), 147–169.

[4]

Boian Alexandrov, Velimir Valentinov Vesselinov, and Hristo Nikolov Djidjev. 2018. Non-negative Tensor Factorization for Robust Exploratory Big-Data Analytics. Technical Report. Los Alamos National Laboratory (LANL), Los Alamos, NM.

[5]

Ethem Alpaydin. 2020. Introduction to Machine Learning. MIT Press, Cambridge, MA.

Digital Library

[6]

Adriano O. Andrade, Slawomir Nasuto, Peter Kyberd, and Catherine M. Sweeney-Reed. 2005. Generative topographic mapping applied to clustering and visualization of motor unit action potentials. Biosystems 82, 3 (2005), 273–284.

[7]

Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein generative adversarial networks. In Proc. of ICML. 214–223.

[8]

Charles M. Bachmann, Thomas L. Ainsworth, and Robert A. Fusina. 2005. Exploiting manifold geometry in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 43, 3 (2005), 441–454.

[9]

Mukund Balasubramanian and Eric L. Schwartz. 2002. The Isomap algorithm and topological stability. Science 295 (2002), 7.

[10]

Pierre Baldi. 2012. Autoencoders, unsupervised learning, and deep architectures. In Proc. of UTLW. 37–49.

[11]

Pierre Baldi. 2012. Boolean autoencoders and hypercube clustering complexity. Designs, Codes and Cryptography 65, 3 (2012), 383–403.

Digital Library

[12]

Pierre Baldi and Zhiqin Lu. 2012. Complex-valued autoencoders. Neural Networks 33 (2012), 136–147.

Digital Library

[13]

Jianmin Bao, Dong Chen, Fang Wen, Houqiang Li, and Gang Hua. 2017. CVAE-GAN: Fine-grained image generation through asymmetric training. In Proc. of ICCV. 2745–2754.

[14]

G. Barello, Adam Charles, and Jonathan Pillow. 2018. Sparse-coding variational auto-encoders. bioRxiv (2018), 399246.

[15]

Mikhail Belkin and Partha Niyogi. 2003. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15, 6 (2003), 1373–1396.

Digital Library

[16]

Robert M. Bell and Yehuda Koren. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explorations 9, 2 (2007), 75–79.

Digital Library

[17]

Adel Belouchrani, Karim Abed-Meraim, J.-F. Cardoso, and Eric Moulines. 1997. A blind source separation technique using second-order statistics. IEEE Transactions on Signal Processing 45, 2 (1997), 434–444.

Digital Library

[18]

Yoshua Bengio. 2009. Learning Deep Architectures for AI. Foundations and Trends® in Machine Learning. Now Publishers.

[19]

Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 8 (2013), 1798–1828.

Digital Library

[20]

Yoshua Bengio, Hugo Larochelle, and Pascal Vincent. 2006. Non-local manifold Parzen windows. In Proc. of NIPS. 115–122.

[21]

Yoshua Bengio and Martin Monperrus. 2005. Non-local manifold tangent learning. In Proc. of NIPS. 129–136.

[22]

Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent. 2013. Generalized denoising auto-encoders as generative models. In Proc. of NIPS. 899–907.

[23]

David Berthelot, Thomas Schumm, and Luke Metz. 2017. Began: Boundary equilibrium generative adversarial networks. arXiv:1703.10717

[24]

Filippo Maria Bianchi, Daniele Grattarola, and Cesare Alippi. 2020. Spectral clustering with graph neural networks for graph pooling. arXiv:1907.00481

[25]

Christopher M. Bishop, Markus Svensén, and Christopher K. I. Williams. 1998. GTM: The generative topographic mapping. Neural Computation 10, 1 (1998), 215–234.

Digital Library

[26]

Christopher M. Bishop, Markus Svensén, and Christopher K. I. Williams. 1998. Developments of the generative topographic mapping. Neurocomputing 21, 1-3 (1998), 203–224.

[27]

David M. Blei, Thomas L. Griffiths, and Michael I. Jordan. 2010. The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Journal of the ACM 57, 2 (2010), 1–30.

Digital Library

[28]

David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe. 2017. Variational inference: A review for statisticians. Journal of the American Statistical Association 112, 518 (2017), 859–877.

[29]

David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3 (Jan. 2003), 993–1022.

Digital Library

[30]

Charles Boncelet. 2009. Image noise models. In The Essential Guide to Image Processing. Elsevier, 143–167.

[31]

William M. Boothby. 1986. An Introduction to Differentiable Manifolds and Riemannian Geometry. Vol. 120. Academic Press.

[32]

Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew Dai, Rafal Jozefowicz, and Samy Bengio. 2016. Generating sentences from a continuous space. In Proc. of CoNLL. 10–21.

[33]

Matthew Brand. 2003. Charting a manifold. In Proc. of NIPS. 985–992.

[34]

R. A. Brualdi and H. J. Ryser. 1991. Combinatorial Matrix Theory. Cambridge University Press.

[35]

Yuri Burda, Roger B. Grosse, and R. Salakhutdinov. 2016. Importance weighted autoencoders. arXiv:1509.00519

[36]

Longbing Cao. 2017. Data science: A comprehensive overview. ACM Computing Surveys 50, 3 (2017), 43.

Digital Library

[37]

Longbing Cao and Chengqi Zhang. 2008. Domain driven data mining. In Data Mining and Knowledge Discovery Technologies. IGI Global, 196–223.

[38]

Shih-Sian Cheng, Hsin-Chia Fu, and Hsin-Min Wang. 2009. Model-based clustering by probabilistic self-organizing maps. IEEE Transactions on Neural Networks 20, 5 (2009), 805–826.

Digital Library

[39]

Kyung Hyun Cho, Alexander Ilin, and Tapani Raiko. 2011. Improved learning of Gaussian-Bernoulli restricted Boltzmann machines. In Proc. of ICANN. 10–17.

[40]

Kyung Hyun Cho, Tapani Raiko, and Alexander Ilin. 2013. Gaussian-Bernoulli deep Boltzmann machine. In Proc. of IJCNN. 1–7.

[41]

Eunsuk Chong and Frank Chongwoo Park. 2017. Movement prediction for a lower limb exoskeleton using a conditional restricted Boltzmann machine. Robotica 35, 11 (2017), 2177–2200.

[42]

Andrzej Cichocki. 2018. Tensor networks for dimensionality reduction, big data and deep learning. In Advances in Data Analysis with Computational Intelligence Methods. Springer, 3–49.

[43]

Andrzej Cichocki, Rafal Zdunek, Anh Huy Phan, and Shun-Ichi Amari. 2009. Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. John Wiley & Sons.

Digital Library

[44]

Adam Coates, Andrew Ng, and Honglak Lee. 2011. An analysis of single-layer networks in unsupervised feature learning. In Proc. of AISTATS. 215–223.

[45]

Pierre Comon. 1994. Independent component analysis, a new concept? Signal Processing 36, 3 (1994), 287–314.

Digital Library

[46]

Michael A. A. Cox and Trevor F. Cox. 2008. Multidimensional scaling. In Handbook of Data Visualization. Springer, 315–347.

[47]

Antonia Creswell and Anil Anthony Bharath. 2018. Denoising adversarial autoencoders. IEEE Transactions on Neural Networks and Learning Systems 30, 4 (2018), 1–17.

[48]

Lieven De Lathauwer, Bart De Moor, and Joos Vandewalle. 2000. A multilinear singular value decomposition. SIAM Journal on Matrix Analysis and Applications 21, 4 (2000), 1253–1278.

Digital Library

[49]

Bart L. R. De Moor and Gene H. Golub. 1991. The restricted singular value decomposition: Properties and applications. SIAM Journal on Matrix Analysis and Applications 12, 3 (1991), 401–425.

Digital Library

[50]

D. de Ridder and V. Franc. 2003. Robust Manifold Learning. Czech Technical University.

[51]

Pietro DeLellis, Giovanni Polverino, Gozde Ustuner, Nicole Abaid, Simone Macrì, Erik M. Bollt, and Maurizio Porfiri. 2014. Collective behaviour across animal species. Scientific Reports 4 (2014), 3723.

[52]

Emily L. Denton, Soumith Chintala, Arthur Szlam, and Rob Fergus. 2015. Deep generative image models using a Laplacian pyramid of adversarial networks. In Proc. of NIPS. 1486–1494.

[53]

Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell. 2017. Adversarial feature learning. In Proc. of ICLR.

[54]

David L. Donoho and Carrie Grimes. 2002. When does geodesic distance recover the true hidden parametrization of families of articulated images?. In Proc. of ESANN. 199–204.

[55]

David L. Donoho and Carrie Grimes. 2003. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proceedings of the National Academy of Sciences 100, 10 (2003), 5591–5596.

[56]

Alexey Dosovitskiy and Thomas Brox. 2016. Generating images with perceptual similarity metrics based on deep networks. In Proc. of NIPS. 658–666.

[57]

Stéphane Dray and Julie Josse. 2015. Principal component analysis with missing values: A comparative survey of methods. Plant Ecology 216, 5 (2015), 657–667.

[58]

Ishan P. Durugkar, Ian Gemp, and Sridhar Mahadevan. 2017. Generative multi-adversarial networks. In Proc. of ICLR.

[59]

Anderson A. Ferreira, Marcos André Gonçalves, and Alberto H. F. Laender. 2012. A brief survey of automatic methods for author name disambiguation. ACM SIGMOD Record 41, 2 (2012), 15–26.

Digital Library

[60]

Asja Fischer and Christian Igel. 2014. Training restricted Boltzmann machines: An introduction. Pattern Recognition 47, 1 (2014).

[61]

Yoav Freund. 1995. Boosting a weak learning algorithm by majority. Information and Computation 121, 2 (1995), 256–285.

Digital Library

[62]

Rui Gao, Xingsong Hou, Jie Qin, Jiaxin Chen, Li Liu, Fan Zhu, Zhao Zhang, and Ling Shao. 2020. Zero-VAE-GAN: Generating unseen features for generalized and transductive zero-shot learning. IEEE Transactions on Image Processing 29 (2020), 3665–3680.

Digital Library

[63]

Xinjian Gao, Zhao Zhang, Tingting Mu, Xudong Zhang, Chaoran Cui, and Meng Wang. 2020. Self-attention driven adversarial similarity learning network. Pattern Recognition 105 (2020), 107331.

[64]

Adam E. Gawęda, Janusz Kacprzyk, Leszek Rutkowski, and Gary G. Yen. 2017. Advances in Data Analysis with Computational Intelligence Methods: Dedicated to Professor Jacek Żurada. Vol. 738. Springer.

[65]

Angelo Genovese, Vincenzo Piuri, Konstantinos N. Plataniotis, and Fabio Scotti. 2019. PalmNet: Gabor-PCA convolutional networks for touchless palmprint recognition. IEEE Transactions on Information Forensics and Security 14, 12 (2019), 3160–3174.

[66]

A. Gepperth and B. Pfülb. 2020. A rigorous link between self-organizing maps and Gaussian mixture models. In Proc. of ICANN.

[67]

Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proc. of AISTATS. 249–256.

[68]

Jacob Goldberger, Geoffrey E. Hinton, Sam T. Roweis, and Ruslan R. Salakhutdinov. 2005. Neighbourhood components analysis. In Proc. of NIPS. 513–520.

[69]

Ian Goodfellow, Mehdi Mirza, Aaron Courville, and Yoshua Bengio. 2013. Multi-prediction deep Boltzmann machines. In Proc. of NIPS. 548–556.

[70]

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proc. of NIPS. 2672–2680.

[71]

Daniele Grattarola, Daniele Zambon, Lorenzo Livi, and Cesare Alippi. 2019. Change detection in graph streams by learning graph embeddings on constant-curvature manifolds. IEEE Transactions on Neural Networks and Learning Systems 31, 6 (2019), 1856–1869.

[72]

Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum, and David M. Blei. 2004. Hierarchical topic models and the nested Chinese restaurant process. In Proc. of NIPS. 17–24.

[73]

Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C. Courville. 2017. Improved training of Wasserstein GANs. In Proc. of NIPS. 5767–5777.

[74]

Richard A. Harshman. 1970. Foundations of the PARAFAC procedure: Models and conditions for an “explanatory” multimodal factor analysis. UCLA Working Papers in Phonetics 16 (1970), 1–84.

[75]

Trevor Hastie and Werner Stuetzle. 1989. Principal curves. Journal of the American Statistical Association 84, 406 (1989), 502–516.

[76]

Junxian He, Daniel Spokoyny, Graham Neubig, and Taylor Berg-Kirkpatrick. 2019. Lagging inference networks and posterior collapse in variational autoencoders. arXiv:1901.05534

[77]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proc. of CVPR. 770–778.

[78]

Irina Higgins, Loïc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017. beta-VAE: Learning basic visual concepts with a constrained variational framework. In Proc. of ICLR.

[79]

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury. 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine 29, 6 (Nov. 2012), 82–97.

[80]

Geoffrey E. Hinton. 2002. Training products of experts by minimizing contrastive divergence. Neural Computation 14, 8 (2002), 1771–1800.

Digital Library

[81]

Geoffrey E. Hinton. 2012. A practical guide to training restricted Boltzmann machines. In Neural Networks: Tricks of the Trade. Vol. 7700. Springer, 599–619.

[82]

Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh. 2006. A fast learning algorithm for deep belief nets. Neural Computation 18, 7 (2006), 1527–1554.

Digital Library

[83]

Geoffrey E. Hinton and Sam T. Roweis. 2002. Stochastic neighbor embedding. In Proc. of NIPS. 857–864.

[84]

Geoffrey E. Hinton and Ruslan R. Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. Science 313, 5786 (2006), 504–507.

[85]

Geoffrey E. Hinton, Terrence Joseph Sejnowski, and Tomaso A. Poggio. 1999. Unsupervised Learning: Foundations of Neural Computation. MIT Press, Cambridge, MA.

[86]

Geoffrey E. Hinton and Richard S. Zemel. 1994. Autoencoders, minimum description length and Helmholtz free energy. In Proc. of NIPS. 3–10.

[87]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Low-complexity coding and decoding. In Proc. of TANC. 297–306.

[88]

He Hong. 2007. Multimodal discovering and fusion for sSemantics multimedia analysis. In Proc. of ALPIT. 155–158.

[89]

Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo, and Sungroh Yoon. 2019. How generative adversarial networks and their variants work: An overview. ACM Computing Surveys 52, 1 (2019), 1–43.

[90]

Heng Huang, Xintao Hu, Yu Zhao, Milad Makkie, Qinglin Dong, Shijie Zhao, Lei Guo, and Tianming Liu. 2018. Modeling task fMRI data via deep convolutional autoencoder. IEEE Transactions on Medical Imaging 37, 7 (2018), 1551–1561.

[91]

Lei Huang, Xianglong Liu, Bo Lang, Adams Yu, Yongliang Wang, and Bo Li. 2018. Orthogonal weight normalization: Solution to optimization over multiple dependent Stiefel manifolds in deep neural networks. In Proc. of AAAI.

[92]

Xun Huang, Yixuan Li, Omid Poursaeed, John Hopcroft, and Serge Belongie. 2017. Stacked generative adversarial networks. In Proc. of CVPR. 5077–5086.

[93]

Aapo Hyvärinen, Juha Karhunen, and Erkki Oja. 2004. Independent Component Analysis. Vol. 46. John Wiley & Sons.

[94]

Aapo Hyvärinen and Erkki Oja. 2000. Independent component analysis: Algorithms and applications. Neural Networks 13, 4-5 (2000), 411–430.

Digital Library

[95]

Daniel Jiwoong Im, Sungjin Ahn, Roland Memisevic, and Yoshua Bengio. 2017. Denoising criterion for variational auto-encoding framework. In Proc. of AAAI.

[96]

Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. 2017. Image-to-image translation with conditional adversarial networks. In Proc. of CVPR. 1125–1134.

[97]

Edwin T. Jaynes. 2003. Probability Theory: The Logic of Science. Cambridge University Press.

[98]

Ian Jolliffe. 2011. Principal Component Analysis. Springer.

[99]

Jürgen Jost and Jèurgen Jost. 2008. Riemannian Geometry and Geometric Analysis. Vol. 42005. Springer Science & Business Media.

[100]

Diederik P. Kingma and Max Welling. 2014. Auto-encoding variational Bayes. In Proc. of ICLR.

[101]

Virginia Klema and Alan Laub. 1980. The singular value decomposition: Its computation and some applications. IEEE Transactions on Automatic Control 25, 2 (1980), 164–176.

[102]

Teuvo Kohonen. 1990. The self-organizing map. Proceedings of the IEEE 78, 9 (1990), 1464–1480.

[103]

Teuvo Kohonen. 2013. Essentials of the self-organizing map. Neural Networks 37 (2013), 52–65.

Digital Library

[104]

Tamara G. Kolda and Brett W. Bader. 2009. Tensor decompositions and applications. SIAM Review 51, 3 (2009), 455–500.

Digital Library

[105]

Daphne Koller, Nir Friedman, and Francis Bach. 2009. Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge, MA.

Digital Library

[106]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Proc. of NIPS. 1097–1105.

[107]

Joseph B. Kruskal. 1964. Nonmetric multidimensional scaling: A numerical method. Psychometrika 29, 2 (1964), 115–129.

[108]

Solomon Kullback. 1997. Information Theory and Statistics. Courier Corporation.

Digital Library

[109]

Martin Längkvist, Lars Karlsson, and Amy Loutfi. 2014. A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters 42 (2014), 11–24.

[110]

Hugo Larochelle, Yoshua Bengio, Jérôme Louradour, and Pascal Lamblin. 2009. Exploring strategies for training deep neural networks. Journal of Machine Learning Research 10 (Jan. 2009), 1–40.

[111]

Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther. 2016. Autoencoding beyond pixels using a learned similarity metric. In Proc. of ICML. 1558–1566.

[112]

M. H. C. Law and A. K. Jain. 2006. Incremental nonlinear dimensionality reduction by manifold learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 3 (March 2006), 377–391.

Digital Library

[113]

Daniel D. Lee and H. Sebastian Seung. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401, 6755 (1999), 788–791.

[114]

Daniel D. Lee and H. Sebastian Seung. 2001. Algorithms for non-negative matrix factorization. In Proc. of NIPS. 556–562.

[115]

Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng. 2009. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proc. of ICML. 609–616.

[116]

Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P. Trevino, Jiliang Tang, and Huan Liu. 2017. Feature selection: A data perspective. ACM Computing Surveys 50, 6 (2017), 1–45.

Digital Library

[117]

Wei Li and Andrew McCallum. 2006. Pachinko allocation: DAG-structured mixture models of topic correlations. In Proc. of ICML. 577–584.

Digital Library

[118]

Wu-Jun Li, Dit-Yan Yeung, and Zhihua Zhang. 2009. Probabilistic relational PCA. In Proc. of NIPS. 1123–1131.

[119]

Xuelong Li, Stephen Lin, Shuicheng Yan, and Dong Xu. 2008. Discriminant locally linear embedding with high-order tensor data. IEEE Transactions on Systems, Man, and Cybernetics: Part B (Cybernetics) 38, 2 (2008), 342–352.

Digital Library

[120]

Xin Li, Feipeng Zhao, and Yuhong Guo. 2015. Conditional restricted Boltzmann machines for multi-label learning with incomplete labels. In Proc. of AISTATS. 635–643.

[121]

Kevin Lin, Dianqi Li, Xiaodong He, Zhengyou Zhang, and Ming-Ting Sun. 2017. Adversarial ranking for language generation. In Proc. of NIPS. 3155–3165.

[122]

Stan Lipovetsky. 2009. PCA and SVD with nonnegative loadings. Pattern Recognition 42, 1 (2009), 68–76.

Digital Library

[123]

Lin Liu, Lin Tang, Libo He, Wei Zhou, and Shaowen Yao. 2016. An overview of hierarchical topic modeling. In Proc. of IHMSC. 391–394.

[124]

Linlin Liu, Haijun Zhang, Xiaofei Xu, Zhao Zhang, and Shuicheng Yan. 2019. Collocating clothes with generative adversarial networks cosupervised by categories and attributes: A multidiscriminator framework. IEEE Transactions on Neural Networks and Learning Systems 31, 9 (2019), 3540–3554.

[125]

Ezequiel López-Rubio. 2010. Probabilistic self-organizing maps for continuous data. IEEE Transactions on Neural Networks 21, 10 (2010), 1543–1554.

Digital Library

[126]

Laurens Maaten. 2009. Learning a parametric embedding by preserving local structure. In Proc. of AISTATS. 384–391.

[127]

Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey. 2015. Adversarial autoencoders. arXiv:1511.0564

[128]

Xudong Mao, Qing Li, Haoran Xie, Raymond Y. K. Lau, Zhen Wang, and Stephen Paul Smolley. 2017. Least squares generative adversarial networks. In Proc. of ICCV. 2794–2802.

[129]

Jonathan Masci, Ueli Meier, Dan Cireşan, and Jürgen Schmidhuber. 2011. Stacked convolutional auto-encoders for hierarchical feature extraction. In Proc. of ICANN. 52–59.

[130]

Roland Memisevic and Geoffrey Hinton. 2007. Unsupervised learning of image transformations. In Proc. of CVPR. 1–8.

[131]

Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv:1411.1784

[132]

Volodymyr Mnih, Hugo Larochelle, and Geoffrey E. Hinton. 2011. Conditional restricted Boltzmann machines for structured output prediction. In Proc. of UAI. 514–522.

[133]

Abdel-Rahman Mohamed, George E. Dahl, and Geoffrey Hinton. 2012. Acoustic modeling using deep belief networks. IEEE Transactions on Audio, Speech, and Language Processing 20, 1 (2012), 14–22.

Digital Library

[134]

Bengt Muthén. 2001. Latent variable mixture modeling. New Developments and Techniques in Structural Equation Modeling 2 (2001), 1–33.

[135]

Bengt Muthén. 2004. Latent variable analysis. In The Sage Handbook of Quantitative Methodology for the Social Sciences, D. Kaplan (Ed.). Sage, Newbury Park, CA, 106–109.

[136]

Ian Nabney. 2002. NETLAB: Algorithms for Pattern Recognition. Springer Science & Business Media.

Digital Library

[137]

Andrew Ng. 2011. Sparse autoencoder. CS294A Lecture Notes 72, 2011 (2011), 1–19.

[138]

Anh Nguyen, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, and Jason Yosinski. 2017. Plug & play generative networks: Conditional iterative generation of images in latent space. In Proc. of CVPR. 4467–4477.

[139]

Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune. 2016. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In Proc. of NIPS. 3387–3395.

[140]

Viet-An Nguyen, Jordan L. Ying, and Philip Resnik. 2013. Lexical and hierarchical topic regression. In Proc. of NIPS. 1106–1114.

[141]

Pentti Paatero and Unto Tapper. 1994. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5, 2 (1994), 111–126.

[142]

Christopher C. Paige and Michael A. Saunders. 1981. Towards a generalized singular value decomposition. SIAM Journal on Numerical Analysis 18, 3 (1981), 398–405.

Digital Library

[143]

Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu. 2019. Semantic image synthesis with spatially-adaptive normalization. In Proc. of CVPR. 2337–2346.

[144]

Karl Pearson. 1901. LIII. On lines and planes of closest fit to systems of points in space. London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 2, 11 (1901), 559–572.

[145]

David Peel and Geoffrey J. McLachlan. 2000. Robust mixture modelling using the t distribution. Statistics and Computing 10, 4 (2000), 339–348.

Digital Library

[146]

Yves Petinot, Kathleen McKeown, and Kapil Thadani. 2011. A hierarchical model of web summaries. In Proc. of HLT. 670–675.

[147]

Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and S. S. Iyengar. 2018. A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys 51, 5 (2018), 92.

[148]

Samira Pouyanfar, Yimin Yang, Shu-Ching Chen, Mei-Ling Shyu, and S. S. Iyengar. 2018. Multimedia big data analytics: A survey. ACM Computing Surveys 51, 1 (2018), 1–34.

Digital Library

[149]

Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434

[150]

Douglas A. Reynolds. 2009. Gaussian mixture models. In Encyclopedia of Biometrics, Stan Z. Li and Anil Jain (Eds). Springer, 741.

[151]

Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. In Proc. of ICML.

Digital Library

[152]

Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio. 2011. Contractive auto-encoders: Explicit invariance during feature extraction. In Proc. of ICML. 833–840.

[153]

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional networks for biomedical image segmentation. In Proc. of MICCAI. 234–241.

[154]

Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley, and Shakir Mohamed. 2017. Variational approaches for auto-encoding generative adversarial networks. arXiv:1706.04987

[155]

Sam Roweis and Zoubin Ghahramani. 1999. A unifying review of linear Gaussian models. Neural Computation 11, 2 (1999), 305–345.

Digital Library

[156]

Sam T. Roweis and Lawrence K. Saul. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290, 5500 (2000), 2323–2326.

[157]

David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. 1985. Learning Internal Representations by Error Propagation. Technical Report. La Jolla Institute for Cognitive Science, University of California, San Diego.

[158]

Ruslan Salakhutdinov. 2015. Learning deep generative models. Review of Statistics and Its Application 2 (2015), 361–385.

[159]

Ruslan Salakhutdinov and Geoffrey Hinton. 2009. Deep Boltzmann machines. In Proc. of AISTATS. 448–455.

[160]

Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved techniques for training GANs. In Proc. of NIPS. 2234–2242.

[161]

Lawrence K. Saul and Sam T. Roweis. 2003. Think globally, fit locally: Unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research 4 (June 2003), 119–155.

[162]

Jürgen Schmidhuber. 2015. Deep learning in neural networks: An overview. Neural Networks 61 (2015), 85–117.

Digital Library

[163]

Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller. 1997. Kernel principal component analysis. In Proc. of ICANN. 583–588.

[164]

Blake Shaw and Tony Jebara. 2007. Minimum volume embedding. In Proc. of AISTATS. 460–467.

[165]

Blake Shaw and Tony Jebara. 2009. Structure preserving embedding. In Proc. of ICML. 937–944.

Digital Library

[166]

Jianbo Shi and Jitendra Malik. 2000. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 8 (2000), 888–905.

Digital Library

[167]

Liangcai Shu, Bo Long, and Weiyi Meng. 2009. A latent topic model for complete entity resolution. In Proc. of ICDE. 880–891.

Digital Library

[168]

Vin D. Silva and Joshua B. Tenenbaum. 2003. Global versus local methods in nonlinear dimensionality reduction. In Proc. of NIPS. 721–728.

[169]

P. Smolensky. 1986. Information processing in dynamical systems: Foundations of harmony theory. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations, David E. Rumelhart and James L. McClelland (Eds.). Vol. 1. MIT Press, Cambridge, MA, 194–281.

[170]

Richard Socher, Eric H. Huang, Jeffrey Pennin, Christopher D. Manning, and Andrew Y. Ng. 2011. Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In Proc. of NIPS. 801–809.

[171]

Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, and Christopher D. Manning. 2011. Semi-supervised recursive autoencoders for predicting sentiment distributions. In Proc. of EMNLP. 151–161.

[172]

R. Souvenir and R. Pless. 2005. Manifold clustering. In Proc. of ICCV. 648–653.

[173]

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15, 1 (2014), 1929–1958.

Digital Library

[174]

Nitish Srivastava and Ruslan R. Salakhutdinov. 2012. Multimodal learning with deep Boltzmann machines. In Proc. of NIPS. 2222–2230.

[175]

Jun Sun, Stephen Boyd, Lin Xiao, and Persi Diaconis. 2006. The fastest mixing Markov process on a graph and a connection to a maximum variance unfolding problem. SIAM Review 48, 4 (2006), 681–699.

Digital Library

[176]

Yee W. Teh and Sam T. Roweis. 2003. Automatic alignment of local representations. In Proc. of NIPS. 865–872.

[177]

Joshua B. Tenenbaum, Vin De Silva, and John C. Langford. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290, 5500 (2000), 2319–2323.

[178]

Michael E. Tipping and Christopher M. Bishop. 1999. Probabilistic principal component analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61, 3 (1999), 611–622.

[179]

Ledyard R. Tucker 1964. The extension of factor analysis to three-dimensional matrices. In Contributions to Mathematical Psychology, N. Frederiksen and H. Gulliksen (Eds.). Holt, Rinehart & Winston, New York, NY, 109–127.

[180]

Laurens Van der Maaten. 2014. Accelerating t-SNE using tree-based algorithms. Journal of Machine Learning Research 15, 1 (2014), 3221–3245.

Digital Library

[181]

Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9 (Nov. 2008), 2579–2605.

[182]

Laurens Van der Maaten and Geoffrey Hinton. 2012. Visualizing non-metric similarities in multiple maps. Machine Learning 87, 1 (2012), 33–55.

Digital Library

[183]

Laurens Van der Maaten, Eric Postma, and Jaap Van den Herik. 2009. Dimensionality Reduction: A Comparative Review. Technical Report TiCC-TR 2009-005. Tilburg University.

[184]

Lieven Vandenberghe and Stephen Boyd. 1996. Semidefinite programming. SIAM Review 38, 1 (1996), 49–95.

Digital Library

[185]

Pascal Vincent and Yoshua Bengio. 2003. Manifold Parzen windows. In Proc. of NIPS. 849–856.

[186]

Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proc. of ICML. 1096–1103.

Digital Library

[187]

Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising. Journal of Machine Learning Research 11 (2010), 3371–3408.

Digital Library

[188]

Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro. 2018. High-resolution image synthesis and semantic manipulation with conditional GANs. In Proc. of CVPR. 8798–8807.

[189]

Y. Wang and Y. Zhang. 2013. Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering 25, 6 (June 2013), 1336–1353.

Digital Library

[190]

Yanyan Wei, Zhao Zhang, Jicong Fan, Yang Wang, Shuicheng Yan, and Meng Wang. 2019. DerainCycleGAN: An attention-guided unsupervised benchmark for single image deraining and rainmaking. arXiv:1912.07015

[191]

Yanyan Wei, Zhao Zhang, Haijun Zhang, Jie Qin, and Mingbo Zhao. 2020. Semi-DerainGAN: A new semi-supervised single image deraining network. arXiv:2001.08388

[192]

Kilian Q. Weinberger and Lawrence K. Saul. 2006. Unsupervised learning of image manifolds by semidefinite programming. International Journal of Computer Vision 70, 1 (Oct. 2006), 77–90.

Digital Library

[193]

Kilian Q. Weinberger, Fei Sha, and Lawrence K. Saul. 2004. Learning a kernel matrix for nonlinear dimensionality reduction. In Proc. of ICML. 106.

Digital Library

[194]

Max Welling and Geoffrey E. Hinton. 2002. A new learning algorithm for mean field Boltzmann machines. In Proc. of ICANN. 351–357.

[195]

Christopher K. I. Williams. 2002. On a connection between kernel PCA and metric MDS. Machine Learning 46, 1–3 (2002), 11–19.

Digital Library

[196]

Svante Wold, Kim Esbensen, and Paul Geladi. 1987. Principal component analysis. Chemometrics and Intelligent Laboratory Systems 2, 1–3 (1987), 37–52.

[197]

Edmond Q. Wu, Gui-Rong Zhou, Li-Min Zhu, Chuan-Feng Wei, He Ren, and Richard S. F. Sheng. 2019. Rotated sphere Haar wavelet and deep contractive auto-encoder network with fuzzy Gaussian SVM for pilot’s pupil center detection. IEEE Transactions on Cybernetics 51, 1 (2019), 332–345.

[198]

Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. 2016. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In Proc. of NIPS. 82–90.

[199]

Fei Xiong, Octavia I. Camps, and Mario Sznaier. 2011. Low order dynamics embedding for high dimensional time series. In Proc. of ICCV. 2368–2374.

Digital Library

[200]

Jun Xu, Lei Xiang, Qingshan Liu, Hannah Gilmore, Jianzhong Wu, Jinghai Tang, and Anant Madabhushi. 2016. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Transactions on Medical Imaging 35, 1 (2016), 119–130.

[201]

Weiwei Xu, Xingpeng Jiang, Xiaohua Hu, and Guangrong Li. 2014. Visualization of genetic disease-phenotype similarities by multiple maps t-SNE with Laplacian regularization. BMC Medical Genomics 7, 2 (2014), S1.

[202]

Ming-Hsuan Yang. 2002. Face recognition using extended Isomap. In Proc. of ICIP.

[203]

Chong Ho Yu. 1977. Exploratory data analysis. Methods 2 (1977), 131–160.

[204]

Kai Yu and Tong Zhang. 2010. Improved local coordinate coding using local tangents. In Proc. of ICML. 1215–1222.

[205]

Kai Yu, Tong Zhang, and Yihong Gong. 2009. Nonlinear learning using local coordinate coding. In Proc. of NIPS. 2223–2231.

[206]

C. Zhang, J. Butepage, H. Kjellstrom, and S. Mandt. 2019. Advances in variational inference. IEEE Transactions on Pattern Analysis and Machine Intelligence 41, 8 (Aug. 2019), 2008–2026.

[207]

Chunhong Zhang, Tiantian Li, Zhibin Ren, Zheng Hu, and Yang Ji. 2019. Taxonomy-aware collaborative denoising autoencoder for personalized recommendation. Applied Intelligence 49, 6 (June 2019), 1–18.

Digital Library

[208]

Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. 2019. Self-attention generative adversarial networks. In Proc. of ICML. 7354–7363.

[209]

Yan Zhang, Zhao Zhang, Sheng Li, Jie Qin, Guangcan Liu, Meng Wang, and Shuicheng Yan. 2018. Unsupervised nonnegative adaptive feature extraction for data representation. IEEE Transactions on Knowledge and Data Engineering 31, 12 (2018), 2423–2440.

[210]

Yan Zhang, Zhao Zhang, Jie Qin, Li Zhang, Bing Li, and Fanzhang Li. 2018. Semi-supervised local multi-manifold Isomap by linear embedding for feature extraction. Pattern Recognition 76 (2018), 662–678.

Digital Library

[211]

Yan Zhang, Zhao Zhang, Zheng Zhang, Mingbo Zhao, Li Zhang, Zhengjun Zha, and Meng Wang. 2020. Deep self-representative concept factorization network for representation learning. In Proc. of ICDM. 361.

[212]

Zhao Zhang, Tommy W. S. Chow, and Mingbo Zhao. 2012. M-Isomap: Orthogonal constrained marginal Isomap for nonlinear dimensionality reduction. IEEE Transactions on Cybernetics 43, 1 (2012), 180–191.

[213]

Zhao Zhang, Fanzhang Li, Mingbo Zhao, Li Zhang, and Shuicheng Yan. 2017. Robust neighborhood preserving projection by nuclear/l2, 1-norm regularization for image feature extraction. IEEE Transactions on Image Processing 26, 4 (2017), 1607–1622.

Digital Library

[214]

Zhenyue Zhang and Hongyuan Zha. 2004. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM Journal on Scientific Computing 26, 1 (2004), 313–338.

Digital Library

[215]

Zhao Zhang, Yan Zhang, Sheng Li, Guangcan Liu, Dan Zeng, Shuicheng Yan, and Meng Wang. 2019. Flexible auto-weighted local-coordinate concept factorization: A robust framework for clustering. IEEE Transactions on Knowledge and Data Engineering 33, 4 (2019), 1523–1539.

[216]

Zhao Zhang, Yan Zhang, Guangcan Liu, Jinhui Tang, Shuicheng Yan, and Meng Wang. 2019. Joint label prediction based semi-supervised adaptive concept factorization for robust data representation. IEEE Transactions on Knowledge and Data Engineering5 (2019), 952–970.

[217]

Zhao Zhang, Yan Zhang, Li Zhang, and Shuicheng Yan. 2020. A survey on concept factorization: From shallow to deep representation learning. arXiv:2007.15840

[218]

Guoqiang Zhong, Li-Na Wang, Xiao Ling, and Junyu Dong. 2016. An overview on data representation learning: From traditional feature learning to recent deep learning. Journal of Finance and Data Science 2, 4 (2016), 265–278.

[219]

Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proc. of ICCV. 2223–2232.

[220]

Michael Zibulevsky and Barak A. Pearlmutter. 2001. Blind source separation by sparse decomposition in a signal dictionary. Neural Computation 13, 4 (2001), 863–882.

Digital Library

Cited By

Sindiramutty SPrabagaran KAkbar RHussain MMalik N(2024)Overview of Generative AI Techniques for CybersecurityReshaping CyberSecurity With Generative AI Techniques10.4018/979-8-3693-5415-5.ch001(1-52)Online publication date: 26-Jul-2024
https://doi.org/10.4018/979-8-3693-5415-5.ch001
Chaudhry MGoswami N(2024)Shaping Tomorrow's MindsIntegrating Generative AI in Education to Achieve Sustainable Development Goals10.4018/979-8-3693-2440-0.ch022(386-407)Online publication date: 3-Jun-2024
https://doi.org/10.4018/979-8-3693-2440-0.ch022
Lalaei RMahmoudabadi D(2024)Promoting Project Outcomes: A Development Approach to Generative AI and LLM-Based Software Applications’ DeploymentInternational Journal of Soft Computing and Engineering10.35940/ijsce.D3636.1403072414:3(6-13)Online publication date: 30-Jul-2024
https://doi.org/10.35940/ijsce.D3636.14030724
Show More Cited By

Index Terms

A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation Learning
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

Recommendations

Representation Learning: A Review and New Perspectives

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. ...
Low-Dimensional Data Representation in Data Analysis
Proceedings of the 6th IAPR TC 3 International Workshop on Artificial Neural Networks in Pattern Recognition - Volume 8774

Many Data Analysis tasks deal with data which are presented in high-dimensional spaces, and the 'curse of dimensionality' phenomena is often an obstacle to the use of many methods, including Neural Network methods, for solving these tasks. To avoid ...
Deep learning of representations for unsupervised and transfer learning
UTLW'11: Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning workshop - Volume 27

Deep learning algorithms seek to exploit the unknown structure in the input distribution in order to discover good representations, often at multiple levels, with higher-level learned features defined in terms of lower-level features. The objective is ...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 5

June 2022

719 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3467690

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 July 2021

Accepted: 01 February 2021

Revised: 01 December 2020

Received: 01 December 2019

Published in CSUR Volume 54, Issue 5

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Università degli Studi di Milano
NVIDIA Corporation
EC within the H2020 Program under projects MOSAICrOWN and MARSAL
Italian Ministry of Research within the PRIN program under project HOPE
JPMorgan Chase & Co

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

39
Total Citations
View Citations
1,224
Total Downloads

Downloads (Last 12 months)359
Downloads (Last 6 weeks)22

Reflects downloads up to 10 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sindiramutty SPrabagaran KAkbar RHussain MMalik N(2024)Overview of Generative AI Techniques for CybersecurityReshaping CyberSecurity With Generative AI Techniques10.4018/979-8-3693-5415-5.ch001(1-52)Online publication date: 26-Jul-2024
https://doi.org/10.4018/979-8-3693-5415-5.ch001
Chaudhry MGoswami N(2024)Shaping Tomorrow's MindsIntegrating Generative AI in Education to Achieve Sustainable Development Goals10.4018/979-8-3693-2440-0.ch022(386-407)Online publication date: 3-Jun-2024
https://doi.org/10.4018/979-8-3693-2440-0.ch022
Lalaei RMahmoudabadi D(2024)Promoting Project Outcomes: A Development Approach to Generative AI and LLM-Based Software Applications’ DeploymentInternational Journal of Soft Computing and Engineering10.35940/ijsce.D3636.1403072414:3(6-13)Online publication date: 30-Jul-2024
https://doi.org/10.35940/ijsce.D3636.14030724
Söderström UHedström ELambertsson KMejtoft T(2024)ChatGPT in education: Teachers’ and Students’ viewsProceedings of the European Conference on Cognitive Ergonomics 202410.1145/3673805.3673828(1-10)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3673805.3673828
Pu JDuan HZhao JLong Y(2024)Rules for Expectation: Learning to Generate Rules via Social Environment ModelingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.333452634:8(6874-6887)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TCSVT.2023.3334526
Ye YHao JHou YWang ZXiao SLuo YZeng W(2024)Generative AI for visualization: State of the art and future directionsVisual Informatics10.1016/j.visinf.2024.04.0038:2(43-66)Online publication date: Jun-2024
https://doi.org/10.1016/j.visinf.2024.04.003
Ali OMurray PMomin MDwivedi YMalik T(2024)The effects of artificial intelligence applications in educational settings: Challenges and strategiesTechnological Forecasting and Social Change10.1016/j.techfore.2023.123076199(123076)Online publication date: Feb-2024
https://doi.org/10.1016/j.techfore.2023.123076
Wang RRen PLiu XChang SHuang H(2024)DCTMInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10378561:5Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103785
Wang RWang YLiu XHuang HSun G(2024)Bridging spherical mixture distributions and word semantic knowledge for neural topic modelingExpert Systems with Applications10.1016/j.eswa.2024.124850(124850)Online publication date: Jul-2024
https://doi.org/10.1016/j.eswa.2024.124850
Bedi PDas SGoyal SShaw RGhosh A(2024)Leveraging Generative AI for Personalized Recommendation SystemAdvanced Computing and Intelligent Technologies10.1007/978-981-97-1961-7_40(587-596)Online publication date: 29-May-2024
https://doi.org/10.1007/978-981-97-1961-7_40
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents