Tim Salimans

16.4k citations
14 papers · 2.0k indexed · 3 hit papers · h-index 8

Tim Salimans

12 papers receiving 1.9k citations

Hit Papers

On Distillation of Guide...11720162026201920222505007501000

Peers

Tim Salimans
Comparison fields: 5 of 122
  • Computer Vision and Pattern Recognition 1.2k
  • Media Technology 285
  • Computer Graphics and Computer-Aided Design 107
  • Artificial Intelligence 673
  • Signal Processing 169
Replace Martín Arjovsky with:
Martín Arjovsky United States
Antonio Robles‐Kelly Australia
Jonathan Ho United States
Augustus Odena United States
Zhenkuan Pan China
Β.N. Chatterji India
Linli Xu China
Mubarak Shah United States
Hubert Ramsauer Austria
Dmitry Ulyanov Russia
Tim Salimans relative to Martín Arjovsky United States Martín Arjovsky's profile →
Citations per field
00.5×2.8×
Martín Arjovsky · 1×
Citations per year

Countries citing papers authored by Tim Salimans

Since Specialization
Citations

This map shows the geographic impact of Tim Salimans'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 Tim Salimans with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Salimans more than expected).

Fields of papers citing papers by Tim Salimans

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tim Salimans. 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 Tim Salimans. The network helps show where Tim Salimans may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Tim Salimans, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tim Salimans Line = papers co-authored together Tim Salimans links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1 20250
2
On Distillation of Guided Diffusion Modelsbreakdown →
2023117
3
Image Super-Resolution Via Iterative Refinementbreakdown →
20221022
4
On Density Estimation with Diffusion Models
20211
5 20212
6
On the relationship between Normalising Flows and Variational- and Denoising Autoencoders.
20190
7
Weight normalization: a simple reparameterization to accelerate training of deep neural networks
2016263
8
Improved Variational Inference with Inverse Autoregressive Flowbreakdown →
2016293
9
Improving Variational Autoencoders with Inverse Autoregressive Flow
201616
10 201689
11 2015160
12
Collaborative Learning of Preference Relations
20121
13 201212
14 20123

About Tim Salimans

Tim Salimans is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Statistics and Probability, having authored 14 papers that have together received 2.0k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (5 papers), Multi-Criteria Decision Making (2 papers), Rough Sets and Fuzzy Logic (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Data Management and Algorithms (2 papers), Model Reduction and Neural Networks (2 papers), Gaussian Processes and Bayesian Inference (2 papers) and Global trade and economics (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (1.2k citations), Media Technology (285 citations) and Computer Graphics and Computer-Aided Design (107 citations). Tim Salimans has collaborated with scholars based in United States, Netherlands and United Kingdom. Frequent co-authors include Diederik P. Kingma, Jonathan Ho, Mohammad Norouzi, Chitwan Saharia, William Chan, David J. Fleet, Max Welling, Ilya Sutskever, Xi Chen and Rafał Józefowicz. Their work appears in journals such as Journal of Econometrics, IEEE Transactions on Pattern Analysis and Machine Intelligence, EUR Research Repository (Erasmus University Rotterdam), UvA-DARE (University of Amsterdam) 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.

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