3 sources
One of the most important optimization researchers of the deep-learning era, especially for work that became default infrastructure across nearly every modern training stack.
Researcher Profile
Editor reviewedDiederik P. Kingma
Optimization and generative modeling
Researcher at University of Florida
A foundational figure in generative modeling whose work helped make variational methods and optimization defaults practical for modern deep learning.
Organizations
University of Florida
About This Page
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Last reviewed
March 18, 2026
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01
Variational autoencoders
02
The Adam optimizer
03
Foundational generative-modeling methods used across deep learning
04
Optimization and generative modeling
05
Adam: A Method for Stochastic Optimization
06
Auto-Encoding Variational Bayes
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A foundational researcher in generative modeling and adversarial robustness whose work changed both how models are trained and how their failure modes are studied.
Start HereGenerative Adversarial Nets
1 source
Co-authored DDPM: the modern diffusion-model starting point.
Start HereDenoising Diffusion Probabilistic Models
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Co-authored DDPM: the modern diffusion-model starting point.
Start HereDenoising Diffusion Probabilistic Models
1 source
Co-authored DDPM: the modern diffusion-model starting point.
Start HereDenoising Diffusion Probabilistic Models
1 source
Co-authored the score-based diffusion SDE paper: a key theoretical view of diffusion models.