Diederik P. Kingma
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- Generative Adversarial Networks and Image Synthesis 9
- Image and Signal Denoising Methods 1
- Artificial Intelligence top 0.05%
- Gaussian Processes and Bayesian Inference 6
- Neural Networks and Applications 2
- Bayesian Methods and Mixture Models 2
- Machine Learning and Algorithms 2
- Signal Processing top 0.2%
- Music and Audio Processing 2
- Media Technology top 1%
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- Model Reduction and Neural Networks 6
- Co-authors
- Max WellingShakir MohamedDanilo Jimenez RezendeTim SalimansRuiqi GaoStefano ErmonJonathan HoChenlin Meng
- Journals
- Työväentutkimus Vuosikirja (1 paper)UvA-DARE (University of Amsterdam) (4 papers)International Conference on Learning Representations (2 papers)
- Partner nations
- United StatesNetherlandsUnited Kingdom
In The Last Decade
Diederik P. Kingma
18 papers receiving 11.6k citations
Hit Papers
Peers
Comparison fields: 5 of 193
- Computer Vision and Pattern Recognition 5.3k
- Artificial Intelligence 6.0k
- Signal Processing 1.4k
- Computer Graphics and Computer-Aided Design 356
- Media Technology 384
Countries citing papers authored by Diederik P. Kingma
This map shows the geographic impact of Diederik P. Kingma's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Diederik P. Kingma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diederik P. Kingma more than expected).
Fields of papers citing papers by Diederik P. Kingma
This network shows the impact of papers produced by Diederik P. Kingma. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Diederik P. Kingma. The network helps show where Diederik P. Kingma may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Diederik P. Kingma, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | On Distillation of Guided Diffusion Modelsbreakdown → | 2023 | 117 |
| 2 | On Density Estimation with Diffusion Models | 2021 | 1 |
| 3 | Score-Based Generative Modeling through Stochastic Differential Equations | 2021 | 6 |
| 4 | ICE-BeeM: Identifiable Conditional Energy-Based Deep Models. | 2020 | 2 |
| 5 | 2020 | 16 | |
| 6 | An Introduction to Variational Autoencodersbreakdown → | 2019 | 1357 |
| 7 | 2019 | 141 | |
| 8 | Variational inference & deep learning: A new synthesis | 2017 | 23 |
| 9 | Weight normalization: a simple reparameterization to accelerate training of deep neural networks | 2016 | 263 |
| 10 | Improving Variational Autoencoders with Inverse Autoregressive Flow | 2016 | 16 |
| 11 | 2016 | 89 | |
| 12 | 2015 | 160 | |
| 13 | Variational Recurrent Auto-Encoders | 2014 | 11 |
| 14 | 2014 | 11 | |
| 15 | Semi-Supervised Learning with Deep Generative Modelsbreakdown → | 2014 | 897 |
| 16 | Stochastic Gradient VB and the Variational Auto-Encoder | 2013 | 112 |
| 17 | Auto-Encoding Variational Bayesbreakdown → | 2013 | 8865 |
| 18 | Regularized estimation of image statistics by Score Matching | 2010 | 17 |
About Diederik P. Kingma
Diederik P. Kingma is a scholar working on Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Artificial Intelligence, Signal Processing and Statistics and Probability, having authored 18 papers that have together received 12.1k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (9 papers), Model Reduction and Neural Networks (6 papers), Gaussian Processes and Bayesian Inference (6 papers), Neural Networks and Applications (2 papers), Music and Audio Processing (2 papers), Bayesian Methods and Mixture Models (2 papers), Machine Learning and Algorithms (2 papers) and Image and Signal Denoising Methods (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (5.3k citations), Artificial Intelligence (6.0k citations), Signal Processing (1.4k citations), Computer Graphics and Computer-Aided Design (356 citations) and Media Technology (384 citations). Diederik P. Kingma has collaborated with scholars based in United States, Netherlands and United Kingdom. Frequent co-authors include Max Welling, Shakir Mohamed, Danilo Jimenez Rezende, Tim Salimans, Ruiqi Gao, Stefano Ermon, Jonathan Ho, Chenlin Meng, Robin Rombach and Ilya Sutskever. Their work appears in journals such as Työväentutkimus Vuosikirja, UvA-DARE (University of Amsterdam), International Conference on Learning Representations, Neural Information Processing Systems and arXiv (Cornell University).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.