Fetching the paper…
Reading the bibliography…
Signal measurement appearing in the form of time series is one of the most common types of data used in medical machine learning applications.
R. Bousseljot, D. Kreiseler, and A. Schnabel, “Nutzung der ekg-signaldatenbank cardiodat der ptb über das internet,” 1995
1995
Earlier work this paper cites.
A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,” circulation , vol. 101, no. 23, pp. e215–e220, 2000
2000
Earlier work this paper cites.
G. B. Moody and R. G. 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.
A. Grinsted, J. C. Moore, and S. Jevrejeva, “Application of the cross wavelet transform and wavelet coherence to geophysical time series,” Nonlinear processes in geophysics , vol. 11, no. 5/6, pp. 561–566, 2004
2004
Earlier work this paper cites.
C. Cassisi, P. Montalto, M. Aliotta, A. Cannata, and A. Pulvirenti, “Similarity measures and dimensionality reduction techniques for time series data mining,” Advances in data mining knowledge discovery and applications’(InTech, Rijeka, Croatia, 2012, , pp. 71–96, 2012
2012
Earlier work this paper cites.
L. Deng, “The mnist database of handwritten digit images for machine learning research,” IEEE Signal Processing Magazine , vol. 29, no. 6, pp. 141–142, 2012
2012
Earlier work this paper cites.
X. Cui, D. M. Bryant, and A. L. Reiss, “Nirs-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation,” Neuroimage , vol. 59, no. 3, pp. 2430–2437, 2012
2012
Earlier work this paper cites.
L. J. Ratliff, S. A. Burden, and S. S. Sastry, “Characterization and computation of local nash equilibria in continuous games,” in 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton) . IEEE, 2013, pp. 917–924
2013
Earlier work this paper cites.
D. Ramyachitra and P. Manikandan, “Imbalanced dataset classification and solutions: a review,” International Journal of Computing and Business Research (IJCBR) , vol. 5, no. 4, pp. 1–29, 2014
2014
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,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2016
Earlier work this paper cites.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang et al. , “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 4681–4690
2017
Earlier work this paper cites.
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan, “Unsupervised pixel-level domain adaptation with generative adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3722–3731
2017
Cited alongside, same era.
H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, and D. N. Metaxas, “Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 5907–5915
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
J. Yoon, D. Jarrett, and M. Van der Schaar, “Time-series generative adversarial networks,” 2019
2019
Later among the works it cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4401–4410
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32 , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds. Curran Associates, Inc., 2019, pp. 8024–8035
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…
Cited alongside, same era.
R. Huang, S. Zhang, T. Li, and R. He, “Beyond face rotation: Global and local perception gan for photorealistic and identity preserving frontal view synthesis,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2439–2448
2017
Cited alongside, same era.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2223–2232
2017
Cited alongside, same era.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in International conference on machine learning . PMLR, 2017, pp. 214–223
2017
Cited alongside, same era.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” Advances in neural information processing systems , vol. 30, 2017
2017
Cited alongside, same era.
D. Micucci, M. Mobilio, and P. Napoletano, “Unimib shar: A dataset for human activity recognition using acceleration data from smartphones,” Applied Sciences , vol. 7, no. 10, 2017
2017
Cited alongside, same era.
V. J. Lawhern, A. J. Solon, N. R. Waytowich, S. M. Gordon, C. P. Hung, and B. J. Lance, “Eegnet: a compact convolutional neural network for eeg-based brain–computer interfaces,” Journal of neural engineering , vol. 15, no. 5, p. 056013, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, and J. Choo, “Stargan: Unified generative adversarial networks for multi-domain image-to-image translation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8789–8797
2018
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, no. 11, pp. 139–144, 2020
2020
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Y. Jiang, S. Chang, and Z. Wang, “Transgan: Two pure transformers can make one strong gan, and that can scale up,” in Thirty-Fifth Conference on Neural Information Processing Systems , 2021
2021
Later among the works it cites.
S. Diao, X. Shen, K. Shum, Y. Song, and T. Zhang, “Tilgan: Transformer-based implicit latent gan for diverse and coherent text generation,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 , 2021, pp. 4844–4858
2021
Later among the works it cites.
X. Li and V. Metsis, “Spp-eegnet: An input-agnostic self-supervised eeg representation model for inter-dataset transfer learning,” in International Conference on Computing and Information Technology . Springer, 2022, pp. 173–182
2022
Closest in time.