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Generative adversarial networks (GANs) are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributions.
Statistical theory of extreme values and some practical applications: a series of lectures
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Generating text with recurrent neural networks
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Imagenet classification with deep convolutional neural networks
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Statistical Language Models Based on Neural Networks
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Bornschein, Jörg and Bengio, Yoshua · 2014
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Generative adversarial nets
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Mnih, Andriy and Gregor, Karol · 2014
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Muprop: Unbiased backpropagation for stochastic neural networks
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Deep learning face attributes in the wild
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Unsupervised representation learning with deep convolutional generative adversarial networks
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Blocks and fuel: Frameworks for deep learning
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Generative models and model criticism via optimized maximum mean discrepancy
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Began: Boundary equilibrium generative adversarial networks
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Towards diverse and natural image descriptions via a conditional gan
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Theano: A python framework for fast computation of mathematical expressions
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Deep residual learning for image recognition
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Professor forcing: A new algorithm for training recurrent networks
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The concrete distribution: A continuous relaxation of discrete random variables
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Least squares generative adversarial networks
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Learning in implicit generative models
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Mcgan: Mean and covariance feature matching gan
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Deep and hierarchical implicit models
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Seqgan: sequence generative adversarial nets with policy gradient
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Parametric adversarial divergences are good task losses for generative modeling
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