Alec Radford is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing.
According to data from OpenAlex, Alec Radford has authored 4 papers receiving a total of 417 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Computer Vision and Pattern Recognition, 2 papers in Artificial Intelligence and 1 paper in Signal Processing. Recurrent topics in Alec Radford's work include Multimodal Machine Learning Applications (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Advanced Image and Video Retrieval Techniques (1 paper). Alec Radford is often cited by papers focused on Multimodal Machine Learning Applications (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Advanced Image and Video Retrieval Techniques (1 paper). Alec Radford collaborates with scholars based in . Alec Radford's co-authors include Mark Chen, Ilya Sutskever, Rewon Child, Heewoo Jun, Jeffrey Wu, Chelsea Voss, Gabriel Goh, Shan Carter, Ludwig Schubert and Nick Cammarata and has published in prestigious journals such as International Conference on Machine Learning.
In The Last Decade
Alec Radford
4 papers
receiving
395 citations
Hit Papers
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.
Citations per year, relative to Alec Radford Alec Radford (= 1×)
peers
Michael J. Wilber
Countries citing papers authored by Alec Radford
Since
Specialization
Citations
This map shows the geographic impact of Alec Radford'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 Alec Radford with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alec Radford more than expected).
This network shows the impact of papers produced by Alec Radford. 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 Alec Radford. The network helps show where Alec Radford may publish in the future.
Co-authorship network of co-authors of Alec Radford
This figure shows the co-authorship network connecting the top 25 collaborators of Alec Radford.
A scholar is included among the top collaborators of Alec Radford 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 Alec Radford. Alec Radford is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
4 of 4 papers shown
1.
Ramesh, Aditya, Mikhail Pavlov, Gabriel Goh, et al.. (2021). Zero-Shot Text-to-Image Generation. International Conference on Machine Learning. 8821–8831.6 indexed citations
Jun, Heewoo, Rewon Child, Mark Chen, et al.. (2020). Distribution Augmentation for Generative Modeling. International Conference on Machine Learning. 5006–5019.8 indexed citations
4.
Chen, Mark, Alec Radford, Rewon Child, et al.. (2020). Generative Pretraining From Pixels. International Conference on Machine Learning. 1. 1691–1703.288 indexed citations breakdown →
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.