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Diederik P. (Durk) Kingma

Diederik P. (Durk) Kingma

I do research on principled and scalable methods for machine learning, with a focus on generative models. My contributions include the Variational Autoencoder (VAE), the Adam optimizer, Glow, and Variational Diffusion Models, but please see Scholar for a more complete list. I was part of the founding team of OpenAI in 2015, obtained a PhD (cum laude) from University of Amsterdam in 2017, and joined Google in 2018.

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    Glow: Generative Flow with Invertible 1x1 Convolutions
    Diederik P. Kingma
    Prafulla Dhariwal
    Proceedings of NIPS'18 (2018)
    Learning Sparse Neural Networks through Regularization
    Christos Louizos
    Diederik P. Kingma
    Max Welling
    Proceedings of ICLR'18 (2017)
    Adam: A Method for Stochastic Optimization
    Diederik P. Kingma
    Jimmy Ba
    Proceedings of ICLR'15 (2015)
    Auto-Encoding Variational Bayes
    Diederik P. Kingma
    Max Welling
    Proceedings of ICLR'14 (2014)
    Semi-Supervised Learning with Deep Generative Models
    Diederik P. Kingma
    Shakir Mohamed
    Danilo Jimenez Rezende
    Max Welling
    Proceedings of NIPS'14 (2014)
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