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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
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-differentiationbackpropagation
2015International Conference on Learning Representations (ICLR 2015) — arXiv:1412.6980, https://arxiv.org/abs/1412.6980
Adam: A Method for Stochastic Optimization
Finding: Adam combines the advantages of AdaGrad (handles sparse gradients) and RMSProp (works well in non-stationary settings), and performs well empirically across problems.
optimizationgradient-descent
1984Combinatorica, Vol. 4, No. 4, pp. 373-395 — https://doi.org/10.1007/BF02579150
A new polynomial-time algorithm for linear programming
Finding: Proved the algorithm runs in polynomial time, answering the open question, and claimed practical speed advantages over Simplex for large problems.
convex-optimizationlinear-programming