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.
Understanding Robustness of Transformers for Image Classification
2021227 citationsSrinadh Bhojanapalli, Ayan Chakrabarti et al.2021 IEEE/CVF International Conference on Computer Vision (ICCV)profile →
Rethinking FID: Towards a Better Evaluation Metric for Image Generation
202445 citationsSadeep Jayasumana, Srikumar Ramalingam et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Andreas Veit'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 Andreas Veit with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Veit more than expected).
This network shows the impact of papers produced by Andreas Veit. 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 Andreas Veit. The network helps show where Andreas Veit may publish in the future.
Co-authorship network of co-authors of Andreas Veit
This figure shows the co-authorship network connecting the top 25 collaborators of Andreas Veit.
A scholar is included among the top collaborators of Andreas Veit 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 Andreas Veit. Andreas Veit is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Jayasumana, Sadeep, Srikumar Ramalingam, Andreas Veit, et al.. (2024). Rethinking FID: Towards a Better Evaluation Metric for Image Generation. 9307–9315.45 indexed citations breakdown →
Bhojanapalli, Srinadh, Ayan Chakrabarti, Daniel Gläsner, et al.. (2021). Understanding Robustness of Transformers for Image Classification. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 10211–10221.227 indexed citations breakdown →
7.
Zhang, Jingzhao, Sai Praneeth Karimireddy, Andreas Veit, et al.. (2020). Why are Adaptive Methods Good for Attention Models. Neural Information Processing Systems. 33. 15383–15393.2 indexed citations
8.
Zhang, Jingzhao, Sai Praneeth Karimireddy, Andreas Veit, et al.. (2019). Why ADAM Beats SGD for Attention Models.23 indexed citations
9.
Bagdasaryan, Eugene, Andreas Veit, Yiqing Hua, Deborah Estrin, & Vitaly Shmatikov. (2018). How To Backdoor Federated Learning.. International Conference on Artificial Intelligence and Statistics. 2938–2948.56 indexed citations
10.
Veit, Andreas & Serge Belongie. (2017). Convolutional Networks with Adaptive Computation Graphs.. arXiv (Cornell University).9 indexed citations
11.
Vaish, Rajan, Snehalkumar S. Gaikwad, Andreas Veit, et al.. (2017). Crowd Research. 829–843.33 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.