Joseph Hassoun

461 total citations
4 papers, 56 citations indexed

About

Joseph Hassoun is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications and Hardware and Architecture. According to data from OpenAlex, Joseph Hassoun has authored 4 papers receiving a total of 56 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Computer Vision and Pattern Recognition, 2 papers in Computer Networks and Communications and 2 papers in Hardware and Architecture. Recurrent topics in Joseph Hassoun's work include Advanced Neural Network Applications (3 papers), Parallel Computing and Optimization Techniques (2 papers) and Advanced Image and Video Retrieval Techniques (1 paper). Joseph Hassoun is often cited by papers focused on Advanced Neural Network Applications (3 papers), Parallel Computing and Optimization Techniques (2 papers) and Advanced Image and Video Retrieval Techniques (1 paper). Joseph Hassoun collaborates with scholars based in United States. Joseph Hassoun's co-authors include David Thorsley, Sheng Shen, Sehoon Kim, Woosuk Kwon, Kurt Keutzer, Amir Gholami, Hamzah Abdel-Aziz, Jun Fang, Ali Shafiee and Suren Byna and has published in prestigious journals such as IEEE Transactions on Parallel and Distributed Systems, Journal of Systems Architecture and arXiv (Cornell University).

In The Last Decade

Joseph Hassoun

4 papers receiving 56 citations

Peers

Joseph Hassoun
Comparison fields: 5 of 24
  • Computer Vision and Pattern Recognition 31
  • Artificial Intelligence 30
  • Electrical and Electronic Engineering 14
  • Computer Networks and Communications 6
  • Hardware and Architecture 5
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Aleksei Udovenko Luxembourg
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Citations per field, relative to Joseph Hassoun
Joseph Hassoun · 1×
Citations per year, relative to Joseph Hassoun
Joseph Hassoun · 1×

Countries citing papers authored by Joseph Hassoun

Since Specialization
Citations

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

Fields of papers citing papers by Joseph Hassoun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Joseph Hassoun

This figure shows the co-authorship network connecting the top 25 collaborators of Joseph Hassoun. A scholar is included among the top collaborators of Joseph Hassoun 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 Joseph Hassoun. Joseph Hassoun 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
# Work Indexed citations
1 3
2 49
3 1
4
Near-Lossless Post-Training Quantization of Deep Neural Networks via a Piecewise Linear Approximation
3

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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