Kingma, Diederik P.

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv

Stochastic Gradient Optimization TechniquesAdvanced Optimization Algorithms Research

Cited by 98,946

Auto-Encoding Variational Bayes

Kingma, D. P., & Welling, M. (2013) · arXiv

Gaussian Processes and Bayesian InferenceTopic Modeling

Cited by 20,067

An Introduction to Variational Autoencoders

Kingma, D. P., & Welling, M. (2019) · Foundations and Trends® in Machine Learning

Gaussian Processes and Bayesian InferenceTopic Modeling

Cited by 2,609

Score-Based Generative Modeling through Stochastic Differential Equations

Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2020) · arXiv

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 1,953

Glow: Generative Flow with Invertible 1x1 Convolutions

Kingma, D. P., & Dhariwal, P. (2018) · arXiv

Generative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

Cited by 825

Learning Sparse Neural Networks through $L_0$ Regularization

Louizos, C., Welling, M., & Kingma, D. P. (2017) · arXiv

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 628

PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications

Salimans, T., Karpathy, A., Chen, X., & Kingma, D. P. (2017) · arXiv

Generative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

Cited by 584

Imagen Video: High Definition Video Generation with Diffusion Models

Ho, J., Chan, W., Saharia, C., Whang, J., Gao, R., Gritsenko, A., Kingma, D. P., Poole, B., Norouzi, M., Fleet, D. J., et al. (2022) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 399

Variational Diffusion Models

Kingma, D. P., Salimans, T., Poole, B., & Ho, J. (2021) · arXiv

Generative Adversarial Networks and Image SynthesisImage and Signal Denoising Methods

Cited by 374

On Linear Identifiability of Learned Representations

Roeder, G., Metz, L., & Kingma, D. P. (2020) · arXiv

Neural Networks and ApplicationsBlind Source Separation Techniques

Cited by 21