Diederik P. Kingma
Machine learning researcher, co-author of the Adam optimizer and of the variational autoencoder. Has worked at OpenAI and Google Brain.
Diederik P. Kingma did not write this page.
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Their wordsWe introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments.
↗Kingma & Ba, "Adam: A Method for Stochastic Optimization" (arXiv)arxiv.org All 8 from this paper
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Their wordsThe method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters.
↗Kingma & Ba, "Adam: A Method for Stochastic Optimization" (arXiv)arxiv.org All 8 from this paper
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Their wordsThe method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients.
↗Kingma & Ba, "Adam: A Method for Stochastic Optimization" (arXiv)arxiv.org All 8 from this paper
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Their wordsThe hyper-parameters have intuitive interpretations and typically require little tuning.
↗Kingma & Ba, "Adam: A Method for Stochastic Optimization" (arXiv)arxiv.org All 8 from this paper