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.
StyleGAN-NADA
2022269 citationsRinon Gal, Amit H. Bermano et al.ACM Transactions on Graphicsprofile →
Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models
202387 citationsRinon Gal, Yuval Atzmon et al.ACM Transactions on Graphicsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Gal Chechik'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 Gal Chechik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gal Chechik more than expected).
This network shows the impact of papers produced by Gal Chechik. 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 Gal Chechik. The network helps show where Gal Chechik may publish in the future.
Co-authorship network of co-authors of Gal Chechik
This figure shows the co-authorship network connecting the top 25 collaborators of Gal Chechik.
A scholar is included among the top collaborators of Gal Chechik 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 Gal Chechik. Gal Chechik is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gal, Rinon, et al.. (2023). Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models. ACM Transactions on Graphics. 42(4). 1–13.87 indexed citations breakdown →
10.
Atzmon, Yuval, Felix Kreuk, Uri Shalit, & Gal Chechik. (2020). A causal view of compositional zero-shot recognition. arXiv (Cornell University). 33. 1462–1473.4 indexed citations
11.
Atzmon, Yuval, et al.. (2020). Long-tail learning with attributes. arXiv (Cornell University).1 indexed citations
Shalit, Uri & Gal Chechik. (2014). Coordinate-descent for learning orthogonal matrices through Givens rotations. International Conference on Machine Learning. 548–556.9 indexed citations
14.
Crammer, Koby & Gal Chechik. (2012). Adaptive Regularization for Similarity Measures.. International Conference on Machine Learning.2 indexed citations
15.
Shalit, Uri, Daphna Weinshall, & Gal Chechik. (2012). Online learning in the embedded manifold of low-rank matrices. Journal of Machine Learning Research. 13(1). 429–458.26 indexed citations
16.
Shalit, Uri, Daphna Weinshall, & Gal Chechik. (2010). Online Learning in The Manifold of Low-Rank Matrices. Neural Information Processing Systems. 23. 2128–2136.25 indexed citations
17.
Chechik, Gal, Uri Shalit, Varun Sharma, & Samy Bengio. (2009). An Online Algorithm for Large Scale Image Similarity Learning. neural information processing systems. 22. 306–314.69 indexed citations
18.
O’Rourke, Sean M., Gal Chechik, Robin C. Friedman, & Eleazar Eskin. (2004). Discrete profile alignment via constrained information bottleneck. Neural Information Processing Systems. 17. 1009–1016.1 indexed citations
19.
Chechik, Gal & Naftali Tishby. (2002). Extracting Relevant Structures with Side Information. Neural Information Processing Systems. 15. 881–888.61 indexed citations
20.
Chechik, Gal, Isaac Meilijson, & Eytan Ruppin. (1999). Effective Learning Requires Neuronal Remodeling of Hebbian Synapses. Neural Information Processing Systems. 12. 96–102.2 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.