Kingma, Diederik P.

18 publications

ICLR 2025 Adam-Mini: Use Fewer Learning Rates to Gain More Yushun Zhang, Congliang Chen, Ziniu Li, Tian Ding, Chenwei Wu, Diederik P Kingma, Yinyu Ye, Zhi-Quan Luo, Ruoyu Sun

NeurIPS 2024 EM Distillation for One-Step Diffusion Models Sirui Xie, Zhisheng Xiao, Diederik P. Kingma, Tingbo Hou, Ying Nian Wu, Kevin Murphy, Tim Salimans, Ben Poole, Ruiqi Gao

NeurIPSW 2022 On Distillation of Guided Diffusion Models Chenlin Meng, Ruiqi Gao, Diederik P Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans

ICLR 2021 Learning Energy-Based Models by Diffusion Recovery Likelihood Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P Kingma

ICLR 2021 Score-Based Generative Modeling Through Stochastic Differential Equations Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole

FnTML 2019 An Introduction to Variational Autoencoders Diederik P. Kingma, Max Welling

NeurIPS 2018 Glow: Generative Flow with Invertible 1x1 Convolutions Diederik P. Kingma, Prafulla Dhariwal

ICLR 2018 Learning Sparse Neural Networks Through L_0 Regularization Christos Louizos, Max Welling, Diederik P. Kingma

ICLR 2017 PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma

ICLR 2017 Variational Lossy Autoencoder Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel

NeurIPS 2016 Improved Variational Inference with Inverse Autoregressive Flow Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling

NeurIPS 2016 Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks Tim Salimans, Diederik P. Kingma

ICLR 2015 Adam: A Method for Stochastic Optimization Diederik P. Kingma, Jimmy Ba

NeurIPS 2015 Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma, Tim Salimans, Max Welling

ICLR 2015 Variational Recurrent Auto-Encoders Otto Fabius, Joost R. van Amersfoort, Diederik P. Kingma

ICLR 2014 Auto-Encoding Variational Bayes Diederik P. Kingma, Max Welling

NeurIPS 2014 Semi-Supervised Learning with Deep Generative Models Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling

NeurIPS 2010 Regularized Estimation of Image Statistics by Score Matching Diederik P. Kingma, Yann L. Cun