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Games generalize the single-objective optimization paradigm by introducing different objective functions for different players.
Some methods of speeding up the convergence of iteration methods
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Introductory lectures on convex optimization: A basic course , volume 87
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On the importance of initialization and momentum in deep learning
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Generative adversarial nets
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Conditional generative adversarial nets
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Deep generative image models using a laplacian pyramid of adversarial networks
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Global convergence of the heavy-ball method for convex optimization
E. Ghadimi, H. R. Feyzmahdavian, and M. Johansson · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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Analysis and design of optimization algorithms via integral quadratic constraints
L. Lessard, B. Recht, and A. Packard · 2016
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Asynchrony begets momentum, with an application to deep learning
Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Yellowfin and the art of momentum tuning
J. Zhang and I. Mitliagkas · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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Local saddle point optimization: A curvature exploitation approach
L. Adolphs, H. Daneshmand, A. Lucchi, and T. Hofmann · 2018
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The mechanics of n-player differentiable games
D. Balduzzi, S. Racaniere, J. Martens, J. Foerster, K. Tuyls, and T. Graepel · 2018
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Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Gradient descent GAN optimization is locally stable
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The limit points of (optimistic) gradient descent in min-max optimization
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Training GANs with optimism
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Spectral normalization for generative adversarial networks
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Is generator conditioning causally related to gan performance?
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