David Bau

7.6k total citations · 1 hit paper
36 papers, 3.2k citations indexed

About

David Bau is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Science Applications. According to data from OpenAlex, David Bau has authored 36 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 10 papers in Computer Science Applications. Recurrent topics in David Bau's work include Teaching and Learning Programming (10 papers), Generative Adversarial Networks and Image Synthesis (7 papers) and Explainable Artificial Intelligence (XAI) (5 papers). David Bau is often cited by papers focused on Teaching and Learning Programming (10 papers), Generative Adversarial Networks and Image Synthesis (7 papers) and Explainable Artificial Intelligence (XAI) (5 papers). David Bau collaborates with scholars based in United States, Mexico and United Kingdom. David Bau's co-authors include Lloyd N. Trefethen, Antonio Torralba, Bolei Zhou, Caitlin Kelleher, Jeff Gray, Franklyn Turbak, Josh Sheldon, Jun-Yan Zhu, Aude Oliva and Hendrik Strobelt and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

David Bau

34 papers receiving 3.1k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

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.

Numerical Linear Algebra

1997 2.1k citations Lloyd N. Trefethen, David Bau Society for Industrial and Applied Mathematics eBooks profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
David Bau	699	527	481	423	409	36	3.2k
Daniel Hsu	2.2k 3.1×	703 1.3×	495 1.0×	230 0.5×	222 0.5×	86	4.0k
Matthew West	387 0.6×	85 0.2×	828 1.7×	204 0.5×	430 1.1×	186	4.2k
Daniel Boley	964 1.4×	731 1.4×	223 0.5×	258 0.6×	545 1.3×	117	3.0k
Walter Gander	139 0.2×	547 1.0×	296 0.6×	252 0.6×	325 0.8×	45	2.0k
Yang Wang	285 0.4×	1.6k 3.1×	562 1.2×	505 1.2×	801 2.0×	313	5.8k
Ji Liu	852 1.2×	850 1.6×	1.1k 2.2×	304 0.7×	190 0.5×	162	4.5k
Alexander N. Gorban	680 1.0×	490 0.9×	1.2k 2.5×	403 1.0×	246 0.6×	221	4.9k
Steven T. Smith	458 0.7×	506 1.0×	505 1.0×	328 0.8×	352 0.9×	33	2.6k
V. Paúl Pauca	534 0.8×	895 1.7×	386 0.8×	107 0.3×	161 0.4×	44	2.7k
Robert Sedgewick	2.0k 2.9×	511 1.0×	104 0.2×	380 0.9×	1.1k 2.7×	77	4.8k

Countries citing papers authored by David Bau

Since Specialization

Citations

This map shows the geographic impact of David Bau'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 David Bau with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Bau more than expected).

Fields of papers citing papers by David Bau

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Bau. 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 David Bau. The network helps show where David Bau may publish in the future.

Co-authorship network of co-authors of David Bau

This figure shows the co-authorship network connecting the top 25 collaborators of David Bau. A scholar is included among the top collaborators of David Bau 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 David Bau. David Bau is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Atkinson, David, et al.. (2024). Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs. 9727–9739. 2 indexed citations

Belinkov, Yonatan, et al.. (2024). Unified Concept Editing in Diffusion Models. 5099–5108. 15 indexed citations

Wallace, Byron, et al.. (2023). Future Lens: Anticipating Subsequent Tokens from a Single Hidden State. 548–560.

Klein, S. B., et al.. (2023). Multimodal Neurons in Pretrained Text-Only Transformers. 2854–2859. 2 indexed citations

Bau, David, et al.. (2023). Testing methods of neural systems understanding. Cognitive Systems Research. 82. 101156–101156. 4 indexed citations

Trefethen, Lloyd N. & David Bau. (2022). Numerical Linear Algebra, Twenty-fifth Anniversary Edition. Society for Industrial and Applied Mathematics eBooks. 4 indexed citations

Weisz, Justin D., Mary Lou Maher, Hendrik Strobelt, et al.. (2022). HAI-GEN 2022: 3rd Workshop on Human-AI Co-Creation with Generative Models. 4–6. 7 indexed citations

Santurkar, Shibani, et al.. (2021). Editing a classifier by rewriting its prediction rules. arXiv (Cornell University). 34. 3 indexed citations

Bau, David, et al.. (2021). Simplifying head-related transfer function measurements: A system for use in regular rooms based on free head movements. 1–6. 2 indexed citations

10.

Bau, David & Jacob Andreas. (2021). How Do Neural Sequence Models Generalize? Local and Global Cues for Out-of-Distribution Prediction. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 5513–5526.

11.

Bau, David, Jun-Yan Zhu, Hendrik Strobelt, et al.. (2019). Visualizing and Understanding Generative Adversarial Networks (Extended Abstract).. arXiv (Cornell University). 2 indexed citations

12.

Weintrop, David, David Bau, & Uri Wilensky. (2019). The cloud is the limit: A case study of programming on the web, with the web. International Journal of Child-Computer Interaction. 20. 1–8. 4 indexed citations

13.

Gilpin, Leilani H., et al.. (2018). Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning. arXiv (Cornell University). 56 indexed citations

14.

Zhou, Bolei, David Bau, Aude Oliva, & Antonio Torralba. (2018). Interpreting Visual Representations of Neural Networks via Network Dissection. Journal of Vision. 18(10). 1244–1244. 5 indexed citations

15.

Bau, David. (2015). Droplet, a blocks-based editor for text code. Journal of computing sciences in colleges. 30(6). 138–144. 34 indexed citations

16.

Bau, David. (2015). Integrating Droplet into Applab — Improving the usability of a blocks-based text editor. 30. 55–57. 3 indexed citations

17.

Bau, David, et al.. (2015). A blocks-based editor for HTML code. 30. 83–85. 5 indexed citations

18.

Bau, David & David Bau. (2014). A Preview of Pencil Code. 21–24. 6 indexed citations

19.

Bau, David, et al.. (2013). Multiple Year Extension Program Outcomes & Impacts Through Evaluation. Journal of Extension. 51(1). 2 indexed citations

20.

Zhao, Ming, Jay Yagnik, Hartwig Adam, & David Bau. (2008). Large scale learning and recognition of faces in web videos. 1–7. 34 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.

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