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Daniel Soudry

Daniel Soudry has 2 papers in our library, in 2016, most of them on Efficient models & compression; often with Itay Hubara, Matthieu Courbariaux, Ran El-Yaniv.

2 papers in our library · 2016

OpenAlex · ORCID · Wikidata · h-index 32 · 127 works · 8,803 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 251 times in our library · ⭐ Paper Stars ranking: #929

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Technion – Israel Institute of TechnologyHaifa · Israel2004–2026 · 55 papersEducation 2004–2008 · B.Sc., Electrical EngineeringEducation 2008–2013 · Ph.D., Electrical EngineeringEmployment 2017–2021 · Asst. Prof., Electrical EngineeringEmployment since 2021 · Associate Professor, Electrical and Computer Engineeringcurrent🏢NvidiaSanta Clara · United States2026 · 1 papercurrent🎓Princeton UniversityPrinceton · United States2026 · 1 papercurrent🎓Columbia UniversityNew York · United States2014–2018 · 14 papersEducation 2014–2017 · Post Doc, Statistics Department

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksEngineering · 2 worksComputer Vision and Pattern Recognition · 2 worksElectrical and Electronic Engineering · 2 worksArtificial Intelligence · 1 work

🧭 Topics 1Research subjects and languages the work is about.Efficient models & compression · 1 work

 Works by year

2010s

20162 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 (2016 · 2,143 citations)
    This work tackles the cost of running deep networks on low-power hardware. Modern DNNs depend on energy-hungry GPUs and full-precision arithmetic. The authors propose Binarized Neural Networks (BNNs), in which weights and hidden activations take only the values +1 or −1. These binary values are used both at inference…
  2. Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations (2016 · 1,610 citations)
    The paper tackles the high cost of running deep networks, whose many multiply-accumulate operations make them slow and power-hungry on ordinary hardware and low-power devices.

 Co-authors here

4 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 251 times (of the 149,439 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated

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