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.
A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
2020259 citationsXiaowei Huang, Daniel Kroening et al.Computer Science Reviewprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Daniel Kroening
Since
Specialization
Citations
This map shows the geographic impact of Daniel Kroening'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 Daniel Kroening with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Kroening more than expected).
This network shows the impact of papers produced by Daniel Kroening. 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 Daniel Kroening. The network helps show where Daniel Kroening may publish in the future.
Co-authorship network of co-authors of Daniel Kroening
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Kroening.
A scholar is included among the top collaborators of Daniel Kroening 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 Daniel Kroening. Daniel Kroening is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Huang, Xiaowei, Daniel Kroening, Wenjie Ruan, et al.. (2020). A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability. Computer Science Review. 37. 100270–100270.259 indexed citations breakdown →
Sun, Youcheng, Hana Chockler, Xiaowei Huang, & Daniel Kroening. (2019). Explaining Deep Neural Networks Using Spectrum-Based Fault Localization. arXiv (Cornell University).2 indexed citations
9.
Huang, Xiaowei, Daniel Kroening, Wenjie Ruan, et al.. (2018). A Survey of Safety and Trustworthiness of Deep Neural Networks. arXiv (Cornell University).5 indexed citations
Kroening, Daniel & Michael Tautschnig. (2014). CBMC - C Bounded Model Checker - (Competition Contribution).. 389–391.26 indexed citations
13.
Holzer, Andreas, Daniel Kroening, Christian Schallhart, Michael Tautschnig, & Helmut Veith. (2012). Proving Reachability Using FShell (Competition Contribution). Oxford University Research Archive (ORA) (University of Oxford). 538–541.1 indexed citations
14.
Haller, Leopold, Alberto Griggio, Martin Brain, & Daniel Kroening. (2012). Deciding floating-point logic with systematic abstraction. Oxford University Research Archive (ORA) (University of Oxford). 131–140.28 indexed citations
Kroening, Daniel & Ofer Strichman. (2003). Efficient Computation of Recurrence Diameters. Oxford University Research Archive (ORA) (University of Oxford).8 indexed citations
20.
Dell, Peter, et al.. (1999). The Impact of Hardware Scheduling Mechanismus on the Performance and Cost of Processor Designs. 65–73.2 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.