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Generative adversarial networks (GANs) are pairs of artificial neural networks that are trained one against each other.
Analyzing and improving the image quality of stylegan
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Gould, N. E.-S. J. and Eldredge, N. (1972) · 1972
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The neutral theory of molecular evolution
Kimura, M. (1983) · 1983
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Molecular evolution over the mutational landscape
Gillespie, J. H. (1984) · 1984
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Towards a general theory of adaptive walks on rugged landscapes
Kauffman, S. and Levin, S. (1987) · 1987
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The nearly neutral theory of molecular evolution
Ohta, T. (1992) · 1992
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The causes of molecular evolution
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A genetic algorithm tutorial
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Gradient-based learning applied to document recognition
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The population genetics of adaptation: the adaptation of dna sequences
Orr, H. A. (2002) · 2002
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Soft sweeps: molecular population genetics of adaptation from standing genetic variation
Hermisson, J. and Pennings, P. S. (2005) · 2005
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The evolution of epidemic influenza
Nelson, M. I. and Holmes, E. C. (2007) · 2007
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The evolutionary genetics of emerging viruses
Holmes, E. C. (2009) · 2009
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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A neural algorithm of artistic style
Gatys, L. A., Ecker, A. S., and Bethge, M. (2015) · 2015
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Nips 2016 tutorial: Generative adversarial networks
Goodfellow, I. (2016) · 2016
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Improved techniques for training gans
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The population genetics of drug resistance evolution in natural populations of viral, bacterial and eukaryotic pathogens
In silico vaccine strain prediction for human influenza viruses
Klingen, T. R., Reimering, S., Guzmán, C. A., and McHardy, A. C. (2018) · 2018
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Robustness and evolvability of heterogeneous cell populations
Kucharavy, A., Rubinstein, B., Zhu, J., and Li, R. (2018) · 2018
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Are gans created equal? a large-scale study
Lucic, M., Kurach, K., Michalski, M., Gelly, S., and Bousquet, O. (2018) · 2018
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Viral quasispecies
Domingo, E. and Perales, C. (2019) · 2019
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Deep learning: new computational modelling techniques for genomics
Eraslan, G., Avsec, Ž., Gagneur, J., and Theis, F. J. (2019) · 2019
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The curse of concentration in robust learning: Evasion and poisoning attacks from concentration of measure
Mahloujifar, S., Diochnos, D. I., and Mahmoody, M. (2019) · 2019
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Wilson, B. A., Garud, N. R., Feder, A. F., Assaf, Z. J., and Pennings, P. S. (2016) · 2016
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Arjovsky, M., Chintala, S., and Bottou, L. (2017) · 2017
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Generalization and equilibrium in generative adversarial nets (gans)
Arora, S., Ge, R., Liang, Y., Ma, T., and Zhang, Y. (2017) · 2017
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Began: Boundary equilibrium generative adversarial networks
Berthelot, D., Schumm, T., and Metz, L. (2017) · 2017
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Are rna viruses candidate agents for the next global pandemic? a review
Carrasco-Hernandez, R., Jácome, R., López Vidal, Y., and Ponce de León, S. (2017) · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017) · 2017
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Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., and Paul Smolley, S. (2017) · 2017
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2019) · 2019
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Eigen: Ecologically-inspired genetic approach for neural network structure searching from scratch
Ren, J., Li, Z., Yang, J., Xu, N., Yang, T., and Foran, D. J. (2019) · 2019
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Spatial evolutionary generative adversarial networks
Toutouh, J., Hemberg, E., and O’Reilly, U.-M. (2019) · 2019
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Evolutionary generative adversarial networks
Wang, C., Xu, C., Yao, X., and Tao, D. (2019) · 2019
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Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts
Mollentze, N. and Streicker, D. G. (2020) · 2020
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