--- title: "Ran El-Yaniv — author profile and papers · Reasoning" description: "Ran El-Yaniv: 3 papers on Elamur Research (2016–2025) — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/ran-el-yaniv/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/ran-el-yaniv/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Ran El-Yaniv # Ran El-Yaniv Ran El-Yaniv has 3 papers in our library, in 2016–2025, most of them on [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/); often with [Daniel Soudry](https://md.research.elamur.ai/free/en/authors/daniel-soudry/), [Itay Hubara](https://md.research.elamur.ai/free/en/authors/itay-hubara/), [Matthieu Courbariaux](https://md.research.elamur.ai/free/en/authors/matthieu-courbariaux/). 3 papers in our library · 2016–2025 No OpenAlex profile matched yet. [📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/ran-el-yaniv/#papers) · [🔗 Cited 326 times in our library](https://md.research.elamur.ai/free/en/authors/ran-el-yaniv/cited-by/) · [⭐ Paper Stars ranking: #904](https://md.research.elamur.ai/free/en/paper-stars/page/10/#star-904) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Ran+El-Yaniv) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Ran+El-Yaniv) · [dblp](https://dblp.org/search?q=Ran+El-Yaniv) · [ACL Anthology](https://aclanthology.org/search/?q=Ran+El-Yaniv) ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 7 The field of science this work belongs to (OpenAlex fields).[Computer Science · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Engineering · 2 works](https://md.research.elamur.ai/free/en/shelves/engineering/) · [Computer Vision and Pattern Recognition · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Electrical and Electronic Engineering · 2 works](https://md.research.elamur.ai/free/en/shelves/engineering/electrical-and-electronic-engineering/) · [Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 2 Research subjects and languages the work is about.[Efficient models & compression · 1 work](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/) ## Works by year ### 2020s [**2025** 1 paper](https://md.research.elamur.ai/free/en/authors/ran-el-yaniv/year/2025/) ### 2010s [**2016** 2 papers](https://md.research.elamur.ai/free/en/authors/ran-el-yaniv/year/2016/) ## 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](https://md.research.elamur.ai/free/en/papers/courbariaux-2016-binarized-neural-networks-training-deep/)** *(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](https://md.research.elamur.ai/free/en/papers/hubara-2016-quantized-neural-networks-training-neural/)** *(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. 3. **[Llama-Nemotron: Efficient Reasoning Models](https://md.research.elamur.ai/free/en/papers/bercovich-2025-llama-nemotron-efficient-reasoning-models/)** *(2025 · 76 citations)* Reasoning-oriented language models write long chains of thought, so inference cost increasingly limits how useful they are. ## Co-authors here 6 co-authors with a page, the most papers together first. - [Daniel Soudry](https://md.research.elamur.ai/free/en/authors/daniel-soudry/) ×2 - [Itay Hubara](https://md.research.elamur.ai/free/en/authors/itay-hubara/) ×2 - [Matthieu Courbariaux](https://md.research.elamur.ai/free/en/authors/matthieu-courbariaux/) ×2 - [Yoshua Bengio](https://md.research.elamur.ai/free/en/authors/yoshua-bengio/) ×2 - [Akhiad Bercovich](https://md.research.elamur.ai/free/en/authors/akhiad-bercovich/) - [Itay Levy](https://md.research.elamur.ai/free/en/authors/itay-levy/) ## Cited by Papers of our library cite their works 326 times (of the 149,439 papers whose bibliographies we read); [48 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/ran-el-yaniv/cited-by/). Updated 2 November 2026