Generating Realistic Data for Network Analytics
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[1] Léon Bottou,et al. Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.
[2] Léon Bottou,et al. Wasserstein Generative Adversarial Networks , 2017, ICML.
[3] Yann LeCun,et al. Energy-based Generative Adversarial Network , 2016, ICLR.
[4] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[5] Zhen Wang,et al. Multi-class Generative Adversarial Networks with the L2 Loss Function , 2016, ArXiv.
[6] Ruslan Salakhutdinov,et al. On the Quantitative Analysis of Decoder-Based Generative Models , 2016, ICLR.
[7] Ryan P. Adams,et al. Sandwiching the marginal likelihood using bidirectional Monte Carlo , 2015, ArXiv.
[8] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[9] Yann LeCun,et al. Energy-based Generative Adversarial Networks , 2016, ICLR.
[10] Jean C. Walrand,et al. Knowledge-Defined Networking: Modelització de la xarxa a través de l’aprenentatge automàtic i la inferència , 2016 .