Understanding the difficulty of training deep feedforward ...
deep networks with sigmoids but initialized from unsuper-vised pre-training (e.g. from RBMs) do not suffer from this saturation behavior. Our proposed explanation rests on the hypothesis that the transformation that the lower layers of the randomly initialized network computes initially is
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TPOT: A Tree-based Pipeline Optimization Tool for ...
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Show, Attend and Tell: Neural Image CaptionGeneration …
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Wasserstein Generative Adversarial Networks
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Self-Attention Generative Adversarial Networks
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Generative Adversarial Text to Image Synthesis
proceedings.mlr.pressdeep convolutional decoder networks to generate realistic images.Dosovitskiy et al.(2015) trained a deconvolutional network (several layers of convolution and upsampling) to generate 3D chair renderings conditioned on a set of graph-ics codes indicating shape, position and lighting.Yang et al. (2015) added an encoder network as well as actions ...
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On the di culty of training recurrent neural networks
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Deep Gaussian Processes
proceedings.mlr.pressrepresentational power of a Gaussian process in the same role is significantly greater than that of an RBM. For the GP the corresponding likelihood is over a continuous vari-able, but it is a nonlinear function of the inputs, p(yjx) = N yjf(x);˙2; where N j ;˙2 is a Gaussian density with mean and variance ˙2. In this case the likelihood is ...
Noise-contrastive estimation: A new estimation principle ...
proceedings.mlr.pressated noise y. The estimation principle thus relies on noise with which the data is contrasted, so that we will refer to the new method as “noise-contrastive estima-tion”. In Section 2, we formally define noise-contrastive es-timation, establish fundamental statistical properties, and make the connection to supervised learning ex-plicit.
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Gender Shades: Intersectional Accuracy Disparities in ...
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