Itay Hubara

6.7k total citations · 1 hit paper
10 papers, 791 citations indexed

About

Itay Hubara is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Surgery. According to data from OpenAlex, Itay Hubara has authored 10 papers receiving a total of 791 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 1 paper in Surgery. Recurrent topics in Itay Hubara's work include Advanced Neural Network Applications (5 papers), Neural Networks and Applications (4 papers) and Domain Adaptation and Few-Shot Learning (3 papers). Itay Hubara is often cited by papers focused on Advanced Neural Network Applications (5 papers), Neural Networks and Applications (4 papers) and Domain Adaptation and Few-Shot Learning (3 papers). Itay Hubara collaborates with scholars based in Israel, United States and Switzerland. Itay Hubara's co-authors include Daniel Soudry, Elad Hoffer, Ron Meir, Ron Banner, Niv Giladi, Torsten Hoefler, Tal Ben‐Nun, Yury Nahshan, Yair Hanani and Besma Nejim and has published in prestigious journals such as Semiconductor Science and Technology, Vascular and Endovascular Surgery and Repository for Publications and Research Data (ETH Zurich).

In The Last Decade

Itay Hubara

10 papers receiving 764 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.

Binarized Neural Networks

2016 481 citations Itay Hubara, Daniel Soudry et al. arXiv (Cornell University) profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Itay Hubara Israel	7	515	450	226	42	41	10	791
Yanghan Wang United States	3	694 1.3×	530 1.2×	167 0.7×	27 0.6×	61 1.5×	3	877
Xiaolong Ma United States	15	414 0.8×	322 0.7×	225 1.0×	21 0.5×	53 1.3×	33	696
Kyuyeon Hwang South Korea	10	563 1.1×	549 1.2×	208 0.9×	66 1.6×	74 1.8×	14	900
Ruihao Gong China	14	751 1.5×	666 1.5×	257 1.1×	55 1.3×	79 1.9×	34	1.2k
Suyog Gupta United States	8	456 0.9×	426 0.9×	270 1.2×	58 1.4×	106 2.6×	8	835
Huanrui Yang United States	13	232 0.5×	277 0.6×	252 1.1×	24 0.6×	54 1.3×	31	599
Aojun Zhou China	10	509 1.0×	298 0.7×	103 0.5×	22 0.5×	20 0.5×	25	748
Sungpill Choi South Korea	14	324 0.6×	154 0.3×	438 1.9×	35 0.8×	64 1.6×	36	682
Yiming Hu China	9	399 0.8×	292 0.6×	268 1.2×	44 1.0×	46 1.1×	24	651

Countries citing papers authored by Itay Hubara

Since Specialization

Citations

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

Fields of papers citing papers by Itay Hubara

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Itay Hubara

This figure shows the co-authorship network connecting the top 25 collaborators of Itay Hubara. A scholar is included among the top collaborators of Itay Hubara 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 Itay Hubara. Itay Hubara is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Hubara, Itay, et al.. (2021). Training of quantized deep neural networks using a magnetic tunnel junction-based synapse. Semiconductor Science and Technology. 36(11). 114003–114003. 1 indexed citations

2.

Hubara, Itay, Yury Nahshan, Yair Hanani, Ron Banner, & Daniel Soudry. (2021). Accurate Post Training Quantization With Small Calibration Sets. 4466–4475. 20 indexed citations

3.

Zarkowsky, Devin S., et al.. (2020). Deep Learning and Multivariable Models Select EVAR Patients for Short-Stay Discharge. Vascular and Endovascular Surgery. 55(1). 18–25. 8 indexed citations

4.

Hoffer, Elad, Tal Ben‐Nun, Itay Hubara, et al.. (2020). Augment Your Batch: Improving Generalization Through Instance Repetition. Repository for Publications and Research Data (ETH Zurich). 8126–8135. 90 indexed citations

5.

Banner, Ron, Itay Hubara, Elad Hoffer, & Daniel Soudry. (2018). Scalable methods for 8-bit training of neural networks. arXiv (Cornell University). 31. 5145–5153. 52 indexed citations

6.

Hubara, Itay, Elad Hoffer, & Daniel Soudry. (2018). Quantized Back-Propagation: Training Binarized Neural Networks with Quantized Gradients. 4 indexed citations

7.

Hoffer, Elad, Itay Hubara, & Daniel Soudry. (2017). Train longer, generalize better: closing the generalization gap in large batch training of neural networks. Neural Information Processing Systems. 30. 1731–1741. 48 indexed citations

8.

Hubara, Itay, et al.. (2016). Binarized Neural Networks. arXiv (Cornell University). 29. 4107–4115. 481 indexed citations breakdown →

9.

Hubara, Itay, et al.. (2016). Playing SNES in the Retro Learning Environment. arXiv (Cornell University). 1 indexed citations

10.

Soudry, Daniel, Itay Hubara, & Ron Meir. (2014). Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights. 27. 963–971. 86 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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