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.
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
This map shows the geographic impact of Lior Wolf'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 Lior Wolf with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lior Wolf more than expected).
This network shows the impact of papers produced by Lior Wolf. 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 Lior Wolf. The network helps show where Lior Wolf may publish in the future.
Co-authorship network of co-authors of Lior Wolf
This figure shows the co-authorship network connecting the top 25 collaborators of Lior Wolf.
A scholar is included among the top collaborators of Lior Wolf 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 Lior Wolf. Lior Wolf is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nachmani, Eliya, Yossi Adi, & Lior Wolf. (2020). Voice Separation with an Unknown Number of Multiple Speakers. International Conference on Machine Learning. 1. 7164–7175.58 indexed citations
8.
Wolf, Lior, et al.. (2020). On the Modularity of Hypernetworks. Neural Information Processing Systems. 33. 10409–10419.2 indexed citations
Wolf, Lior, et al.. (2017). Two-Step Disentanglement for Financial Data.. arXiv (Cornell University).3 indexed citations
13.
Wolf, Lior, et al.. (2017). Learning to Align the Source Code to the Compiled Object Code. International Conference on Machine Learning. 2043–2051.7 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.