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
Learning Sparse Neural Networks through Regularization
Adam: A Method for Stochastic Optimization
Auto-Encoding Variational Bayes
Semi-Supervised Learning with Deep Generative Models