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