Diederik

Diederik P. Kingma

#111,892
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Diederik P. Kingma's Academic­Influence.com Rankings

Diederik P. Kingma
Computer Science
#4246
World Rank
#4468
Historical Rank
Algorithms
#131
World Rank
#132
Historical Rank
Machine Learning
#775
World Rank
#786
Historical Rank
Database
#1467
World Rank
#1542
Historical Rank
computer-science Degrees
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  • Computer Science

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Diederik P. Kingma's Published Works

Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
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Published Works

  • Adam: A Method for Stochastic Optimization (2014) (114693)
  • Auto-Encoding Variational Bayes (2013) (21370)
  • Semi-supervised Learning with Deep Generative Models (2014) (2213)
  • Glow: Generative Flow with Invertible 1x1 Convolutions (2018) (2061)
  • Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks (2016) (1521)
  • Improved Variational Inference with Inverse Autoregressive Flow (2016) (1456)
  • An Introduction to Variational Autoencoders (2019) (1065)
  • Variational Dropout and the Local Reparameterization Trick (2015) (1045)
  • Score-Based Generative Modeling through Stochastic Differential Equations (2020) (952)
  • Learning Sparse Neural Networks through L0 Regularization (2017) (814)
  • PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications (2017) (741)
  • Variational Lossy Autoencoder (2016) (576)
  • Markov Chain Monte Carlo and Variational Inference: Bridging the Gap (2014) (492)
  • Variational Autoencoders and Nonlinear ICA: A Unifying Framework (2019) (297)
  • Stochastic Gradient VB and the Variational Auto-Encoder (2013) (260)
  • Variational Recurrent Auto-Encoders (2014) (190)
  • Variational Dropout and the Local Reparameterization Trick (2015) (151)
  • Imagen Video: High Definition Video Generation with Diffusion Models (2022) (148)
  • GPU Kernels for Block-Sparse Weights (2017) (123)
  • How to Train Your Energy-Based Models (2021) (104)
  • Flow Contrastive Estimation of Energy-Based Models (2019) (79)
  • Regularized estimation of image statistics by Score Matching (2010) (63)
  • Learning Energy-Based Models by Diffusion Recovery Likelihood (2020) (61)
  • Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets (2014) (60)
  • Variational inference & deep learning: A new synthesis (2017) (57)
  • ICE-BeeM: Identifiable Conditional Energy-Based Deep Models (2020) (53)
  • Wave-Tacotron: Spectrogram-Free End-to-End Text-to-Speech Synthesis (2020) (50)
  • On Distillation of Guided Diffusion Models (2022) (38)
  • Fast Gradient-Based Inference with Continuous Latent Variable Models in Auxiliary Form (2013) (37)
  • Improving Variational Autoencoders with Inverse Autoregressive Flow (2016) (31)
  • On Linear Identifiability of Learned Representations (2020) (24)
  • On Density Estimation with Diffusion Models (2021) (11)
  • Understanding the Diffusion Objective as a Weighted Integral of ELBOs (2023) (2)
  • Technical Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models (2015) (2)
  • UvA-DARE (Digital Academic Repository) Auto-Encoding Variational Bayes Auto-Encoding Variational Bayes (2014) (1)

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