Fetching the paper…
Reading the bibliography…
Generative adversarial networks (GANs) studies have grown exponentially in the past few years.
1907
Earlier work this paper cites.
1909
Earlier work this paper cites.
T. Bollerslev, “Generalized autoregressive conditional heteroskedasticity,” Journal of Econometrics , vol. 31, no. 3, pp. 307–327, 1986. [Online]. Available: https://EconPapers.repec.org/RePEc:eee:econom:v:31:y:1986:i:3:p:307-327
1986
Earlier work this paper cites.
F. B. Bryant and P. R. Yarnold, “Principal-components analysis and exploratory and confirmatory factor analysis.” in Reading and understanding multivariate statistics. Washington, DC, US: American Psychological Association, 1995, pp. 99–136
1995
Earlier work this paper cites.
G. Dorffner, “Neural networks for time series processing,” Neural Network World , vol. 6, pp. 447–468, 1996
1996
Earlier work this paper cites.
S. Hochreiter and J. Urgen Schmidhuber, “Lstm,” Neural Computation , vol. 9, no. 8, pp. 1735–1780, 1997. [Online]. Available: http://www7.informatik.tu-muenchen.de/{~}hochreit{%}0Ahttp://www.idsia.ch/{~}juergen
1997
Earlier work this paper cites.
H. Fei and F. Tan, “Bidirectional grid long short-term memory (bigridlstm): A method to address context-sensitivity and vanishing gradient,” Algorithms , vol. 11, no. 11, 2018. [Online]. Available: https://www.mdpi.com/1999-4893/11/11/172
1999
Earlier work this paper cites.
B. Malin and L. Sweeney, “Re-identification of DNA through an automated linkage process,” Proceedings. AMIA Symposium , vol. 2001, pp. 423–427, 2001, publisher: American Medical Informatics Association. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/11825223
2001
Earlier work this paper cites.
G. Moody and R. Mark, “The impact of the mit-bih arrhythmia database,” IEEE Engineering in Medicine and Biology Magazine , vol. 20, no. 3, pp. 45–50, 2001
2001
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics . Philadelphia, Pennsylvania, USA: Association for Computational Linguistics, Jul. 2002, pp. 311–318. [Online]. Available: https://www.aclweb.org/anthology/P02-1040
2002
Earlier work this paper cites.
C. Dwork, “Differential privacy,” in Proceedings of the 33rd International Conference on Automata, Languages and Programming - Volume Part II , ser. ICALP’06. Berlin, Heidelberg: Springer-Verlag, 2006, p. 1–12. [Online]. Available: https://doi.org/10.1007/11787006_1
2006
Earlier work this paper cites.
L. van der Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of Machine Learning Research , vol. 9, no. 86, pp. 2579–2605, 2008. [Online]. Available: http://jmlr.org/papers/v9/vandermaaten08a.html
2008
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” University of Toronto, Toronto, Ontario, Tech. Rep. 0, 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition . Miami, FL, USA: IEEE, 2009, pp. 248–255
2009
Earlier work this paper cites.
Y. LeCun and C. Cortes, “MNIST handwritten digit database,” http://yann.lecun.com/exdb/mnist/, 2010. [Online]. Available: http://yann.lecun.com/exdb/mnist/
2010
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on Knowledge and Data Engineering , vol. 22, no. 10, pp. 1345–1359, 2010
2010
Earlier work this paper cites.
K. El Emam, E. Jonker, L. Arbuckle, and B. Malin, “A systematic review of re-identification attacks on health data,” PLoS ONE , vol. 6, 2011
2011
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” J. Mach. Learn. Res. , vol. 13, no. null, p. 723–773, Mar. 2012
2012
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Q. Weinberger, Eds., vol. 27. Montréal, Canada: Curran Associates, Inc., 2014. [Online]. Available: https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf
2014
Earlier work this paper cites.
C. Dwork and A. Roth, “The algorithmic foundations of differential privacy,” Found. Trends Theor. Comput. Sci. , vol. 9, no. 3–4, p. 211–407, aug 2014. [Online]. Available: https://doi.org/10.1561/0400000042
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
S. Bengio, O. Vinyals, N. Jaitly, and N. Shazeer, “Scheduled sampling for sequence prediction with recurrent neural networks,” 2015
2015
Earlier work this paper cites.
Y. Li, K. Swersky, and R. Zemel, “Generative moment matching networks,” 2015
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, and X. Chen, “Improved techniques for training gans,” in Advances in Neural Information Processing Systems , D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, Eds., vol. 29. Barcelona, Spain: Curran Associates, Inc., 2016, pp. 2234–2242. [Online]. Available: https://proceedings.neurips.cc/paper/2016/file/8a3363abe792db2d8761d6403605aeb7-Paper.pdf
2016
Earlier work this paper cites.
I. Goodfellow, “Generative adversarial networks for text,” 2016. [Online]. Available: https://www.reddit.com/r/MachineLearning/comments/40ldq6/generative_adversarial_networks_for_text/
2016
Earlier work this paper cites.
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,” 33rd International Conference on Machine Learning, ICML 2016 , vol. 3, pp. 1681–1690, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
O. Mogren, “C-rnn-gan: Continuous recurrent neural networks with adversarial training,” 2016
2016
Earlier work this paper cites.
A. E. Johnson, T. J. Pollard, L. Shen, L. H. Lehman, M. Feng, M. Ghassemi, B. Moody, P. Szolovits, L. A. Celi, and R. G. Mark, “Mimic-iii, a freely accessible critical care database,” Scientific data , vol. 3, p. 160035, 2016
2016
Earlier work this paper cites.
M. Abadi, H. B. McMahan, A. Chu, I. Mironov, L. Zhang, I. Goodfellow, and K. Talwar, “Deep learning with differential privacy,” Proceedings of the ACM Conference on Computer and Communications Security , vol. 24-28-October-2016, no. Ccs, pp. 308–318, 2016
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
D. Dua and C. Graff, “UCI machine learning repository,” 2017. [Online]. Available: http://archive.ics.uci.edu/ml
2017
Earlier work this paper cites.
L. Yu, W. Zhang, J. Wang, and Y. Yu, “Seqgan: Sequence generative adversarial nets with policy gradient,” in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence , ser. AAAI’17. San Francisco, California, USA: AAAI Press, 2017, p. 2852–2858
2017
Cited alongside, same era.
2017
Cited alongside, same era.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 , vol. 2017-January, pp. 105–114, 2017
2017
Cited alongside, same era.
M. A. F. Pimentel, A. E. W. Johnson, P. H. Charlton, D. Birrenkott, P. J. Watkinson, L. Tarassenko, and D. A. Clifton, “Toward a robust estimation of respiratory rate from pulse oximeters,” IEEE Transactions on Biomedical Engineering , vol. 64, no. 8, pp. 1914–1923, 2017
S. Harada, H. Hayashi, and S. Uchida, “Biosignal Generation and Latent Variable Analysis With Recurrent Generative Adversarial Networks,” IEEE Access , vol. 7, pp. 144 292–144 302, 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8794813/
2019
Later among the works it cites.
F. Fahimi, Z. Zhang, W. B. Goh, K. K. Ang, and C. Guan, “Towards EEG Generation Using GANs for BCI Applications,” in 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) . Chicago, IL, USA: IEEE, May 2019, pp. 1–4. [Online]. Available: https://ieeexplore.ieee.org/document/8834503/
2019
Later among the works it cites.
L. Yi and M. Mak, “Adversarial data augmentation network for speech emotion recognition,” in 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) . Lanzhou, China: IEEE, 2019, pp. 529–534
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” 2017
2017
Cited alongside, same era.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” 2017
2017
Cited alongside, same era.
A. Borji, “Pros and cons of gan evaluation measures,” 2018
2018
Cited alongside, same era.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” 2018
2018
Cited alongside, same era.
European Union, “Data protection act 2018 (section36(2)),” 2018, http://www.irishstatutebook.ie/eli/2018/si/314/made/en/pdf
2018
Cited alongside, same era.
H. A. Dau, E. Keogh, K. Kamgar, C.-C. M. Yeh, Y. Zhu, S. Gharghabi, C. A. Ratanamahatana, Yanping, B. Hu, N. Begum, A. Bagnall, A. Mueen, G. Batista, and Hexagon-ML, “The ucr time series classification archive,” October 2018, https://www.cs.ucr.edu/~eamonn/time_series_data_2018/
2018
Cited alongside, same era.
R. D. Hjelm, A. P. Jacob, T. Che, A. Trischler, K. Cho, and Y. Bengio, “Boundary-seeking generative adversarial networks,” 2018
2018
Cited alongside, same era.
T. J. Pollard, A. E. W. Johnson, J. D. Raffa, L. A. Celi, R. G. Mark, and O. Badawi, “The eICU Collaborative Research Database, a freely available multi-center database for critical care research,” Scientific Data , vol. 5, no. 1, p. 180178, Sep. 2018. [Online]. Available: https://doi.org/10.1038/sdata.2018.178
2018
Cited alongside, same era.
Y. Luo, Y. Zhang, X. Cai, and X. Yuan, “E 2 GAN: End-to-End Generative Adversarial Network for Multivariate Time Series Imputation,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence . Macao, China: International Joint Conferences on Artificial Intelligence Organization, Aug. 2019, pp. 3094–3100. [Online]. Available: https://www.ijcai.org/proceedings/2019/429
2019
Later among the works it cites.
G. Zhu, H. Zhao, H. Liu, and H. Sun, “A Novel LSTM-GAN Algorithm for Time Series Anomaly Detection,” in 2019 Prognostics and System Health Management Conference (PHM-Qingdao) . Qingdao, China: IEEE, Oct. 2019, pp. 1–6
2019
Later among the works it cites.
C. Hardy, E. L. Merrer, and B. Sericola, “Md-gan: Multi-discriminator generative adversarial networks for distributed datasets,” 2019
2019
Later among the works it cites.
J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro, “LOGAN: Membership Inference Attacks Against Generative Models,” Proceedings on Privacy Enhancing Technologies , vol. 2019, no. 1, pp. 133–152, 2019
2019
Later among the works it cites.
J. Gui, Z. Sun, Y. Wen, D. Tao, and J. Ye, “A review on generative adversarial networks: Algorithms, theory, and applications,” 2020
2020
Later among the works it cites.
C. Yinka-Banjo and O.-A. Ugot, “A review of generative adversarial networks and its application in cybersecurity,” Artificial Intelligence Review , vol. 53, no. 3, pp. 1721–1736, Mar. 2020. [Online]. Available: http://link.springer.com/10.1007/s10462-019-09717-4
2020
Later among the works it cites.
Z. Wang, Q. She, A. F. Smeaton, T. E. Ward, and G. Healy, “Synthetic-neuroscore: Using a neuro-ai interface for evaluating generative adversarial networks,” Neurocomputing , vol. 405, pp. 26–36, 2020
2020
Later among the works it cites.
Z. Wang, G. Healy, A. F. Smeaton, and T. E. Ward, “Use of neural signals to evaluate the quality of generative adversarial network performance in facial image generation,” Cognitive Computation , vol. 12, no. 1, pp. 13–24, 2020
2020
Later among the works it cites.
H. Ni, L. Szpruch, M. Wiese, S. Liao, and B. Xiao, “Conditional sig-wasserstein gans for time series generation,” 2020
2020
Later among the works it cites.
H. Sun, Z. Deng, H. Chen, and D. C. Parkes, “Decision-aware conditional gans for time series data,” 2020
2020
Later among the works it cites.
P. Detti, G. Vatti, and G. Zabalo Manrique de Lara, “Eeg synchronization analysis for seizure prediction: A study on data of noninvasive recordings,” Processes , vol. 8, no. 7, 2020. [Online]. Available: https://www.mdpi.com/2227-9717/8/7/846
2020
Later among the works it cites.
D. Kiyasseh, G. A. Tadesse, L. N. T. Nhan, L. Van Tan, L. Thwaites, T. Zhu, and D. Clifton, “PlethAugment: GAN-Based PPG Augmentation for Medical Diagnosis in Low-Resource Settings,” IEEE Journal of Biomedical and Health Informatics , vol. 24, no. 11, pp. 3226–3235, Nov. 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9078801/
2020
Later among the works it cites.
E. Brophy, “Synthesis of dependent multichannel ecg using generative adversarial networks,” in Proceedings of the 29th ACM International Conference on Information &; Knowledge Management , ser. CIKM ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 3229–3232. [Online]. Available: https://doi.org/10.1145/3340531.3418509
2020
Later among the works it cites.
Q. Li, H. Hao, Y. Zhao, Q. Geng, G. Liu, Y. Zhang, and F. Yu, “GANs-LSTM Model for Soil Temperature Estimation From Meteorological: A New Approach,” IEEE Access , vol. 8, pp. 59 427–59 443, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9045947/
2020
Later among the works it cites.
S. Kaushik, A. Choudhury, S. Natarajan, L. A. Pickett, and V. Dutt, “Medicine Expenditure Prediction via a Variance- Based Generative Adversarial Network,” IEEE Access , vol. 8, pp. 110 947–110 958, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9116991/
2020
Later among the works it cites.
A. Kolokolova, M. Billard, R. Bishop, M. Elsisy, Z. Northcott, L. Graves, V. Nagisetty, and H. Patey, “Gans & reels: Creating irish music using a generative adversarial network,” 2020
2020
Later among the works it cites.
P.-S. Cheng, C.-Y. Lai, C.-C. Chang, S.-F. Chiou, and Y.-C. Yang, “A Variant Model of TGAN for Music Generation,” in Proceedings of the 2020 Asia Service Sciences and Software Engineering Conference . Nagoya Japan: ACM, May 2020, pp. 40–45. [Online]. Available: https://dl.acm.org/doi/10.1145/3399871.3399888
2020
Later among the works it cites.
Y. Choi, H. Lim, H. Choi, and I.-J. Kim, “GAN-Based Anomaly Detection and Localization of Multivariate Time Series Data for Power Plant,” in 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) . Busan, Korea (South): IEEE, Feb. 2020, pp. 71–74. [Online]. Available: https://ieeexplore.ieee.org/document/9070362/
2020
Later among the works it cites.
D. Parthasarathy, K. Bäckström, J. Henriksson, and S. Einarsdóttir, “Controlled time series generation for automotive software-in-the-loop testing using gans,” 2020
2020
Later among the works it cites.
D. Pascual, A. Amirshahi, A. Aminifar, D. Atienza, P. Ryvlin, and R. Wattenhofer, “Epilepsygan: Synthetic epileptic brain activities with privacy preservation,” IEEE Transactions on Biomedical Engineering , vol. 67, pp. 1–1, 2020
2020
Later among the works it cites.
H. Zhang, N. Xiao, P. Liu, Z. Wang, and R. Tang, “G-RNN-GAN for Singing Voice Separation,” in Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing . Chengdu China: ACM, May 2020, pp. 69–73. [Online]. Available: https://dl.acm.org/doi/10.1145/3404716.3404718
2020
Later among the works it cites.
F. Qu, J. Liu, Y. Ma, D. Zang, and M. Fu, “A novel wind turbine data imputation method with multiple optimizations based on GANs,” Mechanical Systems and Signal Processing , vol. 139, p. 106610, May 2020. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0888327019308313
2020
Later among the works it cites.
L. Han, K. Zheng, L. Zhao, X. Wang, and H. Wen, “Content-Aware Traffic Data Completion in ITS Based on Generative Adversarial Nets,” IEEE Transactions on Vehicular Technology , vol. 69, no. 10, pp. 11 950–11 962, Oct. 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9133309/
2020
Later among the works it cites.
Y. Chen, Y. Lv, and F.-Y. Wang, “Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks,” IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 4, pp. 1624–1630, Apr. 2020. [Online]. Available: https://ieeexplore.ieee.org/document/8699108/
2020
Later among the works it cites.
S. Augenstein, H. B. McMahan, D. Ramage, S. Ramaswamy, P. Kairouz, M. Chen, R. Mathews, and B. A. y Arcas, “Generative models for effective ml on private, decentralized datasets,” 2020
2020
Later among the works it cites.
M. Rasouli, T. Sun, and R. Rajagopal, “Fedgan: Federated generative adversarial networks for distributed data,” 2020
2020
Later among the works it cites.
Z. Wang, Q. She, and T. E. Ward, “Generative adversarial networks in computer vision: A survey and taxonomy,” ACM Computing Surveys (CSUR) , vol. 54, no. 2, pp. 1–38, 2021
2021
Closest in time.
——, “Pros and cons of gan evaluation measures: New developments,” 2021
2021
Closest in time.
M. Lapata, “Emnlp14,” http://homepages.inf.ed.ac.uk/mlap/Data/EMNLP14/ , 2015, accessed: 2021-04-30
2021
Closest in time.
S. Winiger, “Obama political speech generator - recurrent neural network,” https://github.com/samim23/obama-rnn , 2015, accessed: 2021-04-30
2021
Closest in time.
O.-M. Institute, “Oxford-man institute of quantitative finance realized library,” https://realized.oxford-man.ox.ac.uk , 2021, accessed: 2021-04-30
2021
Closest in time.
D. Hazra and Y.-C. Byun, “SynSigGAN: Generative Adversarial Networks for Synthetic Biomedical Signal Generation,” Biology , vol. 9, no. 12, p. 441, Dec. 2020. [Online]. Available: https://www.mdpi.com/2079-7737/9/12/441
2079
Closest in time.