Ferenc Huszár

23.2k total citations · 2 hit papers
18 papers, 12.4k citations indexed

About

Ferenc Huszár is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Media Technology. According to data from OpenAlex, Ferenc Huszár has authored 18 papers receiving a total of 12.4k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 4 papers in Media Technology. Recurrent topics in Ferenc Huszár's work include Gaussian Processes and Bayesian Inference (7 papers), Advanced Image Processing Techniques (5 papers) and Image Processing Techniques and Applications (4 papers). Ferenc Huszár is often cited by papers focused on Gaussian Processes and Bayesian Inference (7 papers), Advanced Image Processing Techniques (5 papers) and Image Processing Techniques and Applications (4 papers). Ferenc Huszár collaborates with scholars based in United Kingdom, United States and Germany. Ferenc Huszár's co-authors include Wenzhe Shi, Zehan Wang, Andrew P. Aitken, José Caballero, Johannes Totz, Lucas Theis, Andrew Cunningham, Alykhan Tejani, Christian Ledig and Alejandro Acosta and has published in prestigious journals such as Current Biology, Physical Review A and Patterns.

In The Last Decade

Ferenc Huszár

17 papers receiving 12.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

2017 7.5k citations Christian Ledig, Lucas Theis et al. profile →
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

2016 4.3k citations Wenzhe Shi, José Caballero et al. profile →

Peers

Ferenc Huszár

CVPR

MT

AI

RNMI

BE

Johannes Totz United Kingdom

Andrew P. Aitken United Kingdom

Zehan Wang China

Wenzhe Shi United Kingdom

Radu Timofte Switzerland

Kyoung Mu Lee South Korea

Lucas Theis United States

Xinghao Ding China

José Caballero United Kingdom

Johannes Totz United Kingdom

Ferenc Huszár

10.4k ×1.0

CVPR

4.8k ×1.0

MT

925 ×0.8

AI

769 ×1.0

RNMI

730 ×1.1

BE

Citations per year, relative to Ferenc Huszár Ferenc Huszár (= 1×) peers Johannes Totz

Countries citing papers authored by Ferenc Huszár

Since Specialization

Citations

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

Fields of papers citing papers by Ferenc Huszár

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ferenc Huszár

This figure shows the co-authorship network connecting the top 25 collaborators of Ferenc Huszár. A scholar is included among the top collaborators of Ferenc Huszár based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ferenc Huszár. Ferenc Huszár is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

18 of 18 papers shown

1.

Huszár, Ferenc, et al.. (2024). Do Finetti: On Causal Effects for Exchangeable Data. 127317–127345.

2.

Venieris, Stylianos I., et al.. (2024). Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation. 5 indexed citations

3.

Lazovich, T., et al.. (2022). Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics. Patterns. 3(8). 100568–100568. 8 indexed citations

4.

Huszár, Ferenc, Lucas Theis, Wenzhe Shi, & Andrew Cunningham. (2020). Lossy Image Compression with Compressive Autoencoders. Apollo (University of Cambridge). 94 indexed citations

5.

Zhang, Caojin, Yuanpu Xie, Sofia Ira Ktena, et al.. (2020). Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems. 521–526. 25 indexed citations

6.

Ktena, Sofia Ira, et al.. (2020). Deep Bayesian Bandits: Exploring in Online Personalized Recommendations. 456–461. 17 indexed citations

7.

Ktena, Sofia Ira, et al.. (2019). Addressing delayed feedback for continuous training with neural networks in CTR prediction. 187–195. 30 indexed citations

8.

Korshunova, Iryna, Jonas Degrave, Ferenc Huszár, et al.. (2018). BRUNO: A Deep Recurrent Model for Exchangeable Data. arXiv (Cornell University). 31. 7190–7198. 5 indexed citations

9.

Ledig, Christian, Lucas Theis, Ferenc Huszár, et al.. (2017). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. 105–114. 7534 indexed citations breakdown →

10.

Sønderby, Casper Kaae, J. A. Caballero, Lucas Theis, Wenzhe Shi, & Ferenc Huszár. (2016). Amortised MAP Inference for Image Super-resolution. arXiv (Cornell University). 97 indexed citations

11.

Shi, Wenzhe, José Caballero, Ferenc Huszár, et al.. (2016). Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. 1874–1883. 4297 indexed citations breakdown →

12.

Houlsby, Neil, Ferenc Huszár, Mohammad M. Ghassemi, et al.. (2013). Cognitive Tomography Reveals Complex, Task-Independent Mental Representations. Current Biology. 23(21). 2169–2175. 39 indexed citations

13.

Kravtsov, Konstantin, S. S. Straupe, I. V. Radchenko, et al.. (2013). Experimental adaptive Bayesian tomography. Physical Review A. 87(6). 55 indexed citations

14.

Houlsby, Neil, Ferenc Huszár, Zoubin Ghahramani, & José Miguel Hernández-Lobato. (2012). Collaborative Gaussian Processes for Preference Learning. Cambridge University Engineering Department Publications Database. 25. 2096–2104. 45 indexed citations

15.

Huszár, Ferenc & David Duvenaud. (2012). Optimally-Weighted Herding is Bayesian Quadrature. arXiv (Cornell University). 377–386. 11 indexed citations

16.

Huszár, Ferenc & Neil Houlsby. (2012). Adaptive Bayesian quantum tomography. Physical Review A. 85(5). 105 indexed citations

17.

Lacoste-Julien, Simon, Ferenc Huszár, & Zoubin Ghahramani. (2011). Approximate inference for the loss-calibrated Bayesian. Cambridge University Engineering Department Publications Database. 416–424. 11 indexed citations

18.

Huszár, Ferenc, Uta Noppeney, & Máté Lengyel. (2010). Mind Reading by Machine Learning: A Doubly Bayesian Method for Inferring Mental Representations. eScholarship (California Digital Library). 32(32). 2810–2815. 9 indexed citations

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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