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3 papers

2017Journal of Machine Learning Research (JMLR), Vol. 18, pp. 1-43 — https://jmlr.org/papers/v18/17-468.html

Automatic Differentiation in Machine Learning: a Survey

Alexey Andreyevich Radul, Atılım Güneş Baydin, Jeffrey Mark Siskind, Barak A. Pearlmutter

Finding: Reverse-mode AD is the foundational algorithm that powers the training of virtually all deep learning models — commonly known in the ML community as backpropagation.

automatic-differentiation backpropagation

2015International Conference on Learning Representations (ICLR 2015) — arXiv:1412.6980, https://arxiv.org/abs/1412.6980

Adam: A Method for Stochastic Optimization

Jimmy Ba, Diederik P. Kingma

Finding: Adam combines the advantages of AdaGrad (handles sparse gradients) and RMSProp (works well in non-stationary settings), and performs well empirically across problems.

optimization gradient-descent

1986Nature, Vol. 323, pp. 533-536 — https://doi.org/10.1038/323533a0

Learning representations by back-propagating errors

Ronald J. Williams, Geoffrey E. Hinton, David E. Rumelhart

Finding: Hidden units learn to represent important task features on their own (e.g. 'person', 'generation', 'nationality' in a family-tree problem) — deep networks learn abstract representations, not just memorize.

backpropagation neural-networks