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Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning.
Creating artificial neural networks that generalize
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Training with noise is equivalent to tikhonov regularization
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Stochastic backpropagation and approximate inference in deep generative models
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Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
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Are disentangled representations helpful for abstract visual reasoning?
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