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
2016481 citationsItay Hubara, Daniel Soudry et al.arXiv (Cornell University)profile →
Citations per year, relative to Itay Hubara Itay Hubara (= 1×)
peers
Yanghan Wang
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).
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.
Hubara, Itay, Yury Nahshan, Yair Hanani, Ron Banner, & Daniel Soudry. (2021). Accurate Post Training Quantization With Small Calibration Sets. 4466–4475.20 indexed citations
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
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.