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Training neural networks is traditionally done by providing a sequence of random mini-batches sampled uniformly from the entire training data.
Reinforcement today
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Comparing support vector machines with gaussian kernels to radial basis function classifiers
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Krizhevsky, A. and Hinton, G · 2009
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Flexible shaping: How learning in small steps helps
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Self-paced learning for latent variable models
Kumar, M. P., Packer, B., and Koller, D · 2010
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Conditioned reflexes: an investigation of the physiological activity of the cerebral cortex
Pavlov, P. I · 2010
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Understanding machine learning: From theory to algorithms
Shalev-Shwartz, S. and Ben-David, S · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Rethinking the inception architecture for computer vision
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Active bias: Training more accurate neural networks by emphasizing high variance samples
Chang, H.-S., Learned-Miller, E., and McCallum, A · 2017
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Reverse curriculum generation for reinforcement learning
Florensa, C., Held, D., Wulfmeier, M., Zhang, M., and Abbeel, P · 2017
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Automated curriculum learning for neural networks
Graves, A., Bellemare, M. G., Menick, J., Munos, R., and Kavukcuoglu, K · 2017
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