1.8k total citations 10 papers, 256 citations indexed
About
Elad Hoffer is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics.
According to data from OpenAlex, Elad Hoffer has authored 10 papers receiving a total of 256 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 2 papers in Statistical and Nonlinear Physics. Recurrent topics in Elad Hoffer's work include Domain Adaptation and Few-Shot Learning (6 papers), Advanced Neural Network Applications (4 papers) and Neural Networks and Applications (3 papers). Elad Hoffer is often cited by papers focused on Domain Adaptation and Few-Shot Learning (6 papers), Advanced Neural Network Applications (4 papers) and Neural Networks and Applications (3 papers). Elad Hoffer collaborates with scholars based in Israel, United States and Switzerland. Elad Hoffer's co-authors include Daniel Soudry, Itay Hubara, Ron Banner, Niv Giladi, Torsten Hoefler, Tal Ben‐Nun, Yury Nahshan and Nir Ailon and has published in prestigious journals such as Repository for Publications and Research Data (ETH Zurich), arXiv (Cornell University) and Neural Information Processing Systems.
In The Last Decade
Elad Hoffer
10 papers
receiving
245 citations
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Elad Hoffer'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 Elad Hoffer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Elad Hoffer more than expected).
This network shows the impact of papers produced by Elad Hoffer. 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 Elad Hoffer. The network helps show where Elad Hoffer may publish in the future.
Co-authorship network of co-authors of Elad Hoffer
This figure shows the co-authorship network connecting the top 25 collaborators of Elad Hoffer.
A scholar is included among the top collaborators of Elad Hoffer 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 Elad Hoffer. Elad Hoffer is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
10 of 10 papers shown
1.
Giladi, Niv, et al.. (2020). At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?. arXiv (Cornell University).1 indexed citations
2.
Hoffer, Elad, et al.. (2020). Neural gradients are lognormally distributed: understanding sparse and quantized training.. arXiv (Cornell University).1 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
5.
Banner, Ron, Yury Nahshan, Elad Hoffer, & Daniel Soudry. (2018). ACIQ: Analytical Clipping for Integer Quantization of neural networks. arXiv (Cornell University).40 indexed citations
6.
Hoffer, Elad, et al.. (2018). Norm matters: efficient and accurate normalization schemes in deep networks. Neural Information Processing Systems. 31. 2160–2170.16 indexed citations
7.
Hoffer, Elad, et al.. (2018). Bayesian Gradient Descent: Online Variational Bayes Learning with Increased Robustness to Catastrophic Forgetting and Weight Pruning.. arXiv (Cornell University).1 indexed citations
8.
Hubara, Itay, Elad Hoffer, & Daniel Soudry. (2018). Quantized Back-Propagation: Training Binarized Neural Networks with Quantized Gradients.4 indexed citations
9.
Hoffer, Elad & Nir Ailon. (2017). Semi-supervised deep learning by metric embedding. arXiv (Cornell University).3 indexed citations
10.
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
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.