Judith Bütepage

1.3k total citations · 1 hit paper
11 papers, 682 citations indexed

About

Judith Bütepage is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Judith Bütepage has authored 11 papers receiving a total of 682 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Control and Systems Engineering. Recurrent topics in Judith Bütepage's work include Human Pose and Action Recognition (3 papers), Human Motion and Animation (2 papers) and Explainable Artificial Intelligence (XAI) (2 papers). Judith Bütepage is often cited by papers focused on Human Pose and Action Recognition (3 papers), Human Motion and Animation (2 papers) and Explainable Artificial Intelligence (XAI) (2 papers). Judith Bütepage collaborates with scholars based in Sweden, United States and Switzerland. Judith Bütepage's co-authors include Hedvig Kjellström, Cheng Zhang, Stephan Mandt, Danica Kragić, Michael J. Black, Henrik Boström, Pierre Geurts, Mounia Lalmas, Rishabh Mehrotra and Aasish Pappu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Data Mining and Knowledge Discovery and Data in Brief.

In The Last Decade

Judith Bütepage

7 papers receiving 655 citations

Hit Papers

align trajectories

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.

Advances in Variational Inference

2018 349 citations Cheng Zhang, Judith Bütepage et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →

Peers

Judith Bütepage

CVPR

AI

CSE

BE

SP

Jo-Anne Ting United States

Vikas Singh India

Hakan Çevıkalp Türkiye

R. Frezza Italy

Dit-Yan Yeung Hong Kong

Pablo Zegers Chile

Vandi Verma United States

Andreas Damianou United Kingdom

Ziyu Wang China

Kebo Fu China

Jo-Anne Ting United States

Judith Bütepage

149 ×0.5

CVPR

227 ×0.9

AI

153 ×0.7

CSE

65 ×0.8

BE

38 ×0.7

SP

Citations per year, relative to Judith Bütepage Judith Bütepage (= 1×) peers Jo-Anne Ting

Countries citing papers authored by Judith Bütepage

Since Specialization

Citations

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

Fields of papers citing papers by Judith Bütepage

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Judith Bütepage

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

All Works

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11 of 11 papers shown

1.

Donnelly, William, et al.. (2024). FAST: Filter-Adapted Spatio-Temporal Sampling for Real-Time Rendering. Proceedings of the ACM on Computer Graphics and Interactive Techniques. 7(1). 1–16.

2.

Bütepage, Judith, et al.. (2024). Local Point-wise Explanations of LambdaMART. Linköping electronic conference proceedings. 208. 121–130.

3.

Bütepage, Judith, et al.. (2023). Can local explanation techniques explain linear additive models?. Data Mining and Knowledge Discovery. 38(1). 237–280. 6 indexed citations

4.

Maystre, Lucas, et al.. (2021). Collaborative Classification from Noisy Labels. International Conference on Artificial Intelligence and Statistics. 1639–1647.

5.

Mehrotra, Rishabh, et al.. (2020). Query Understanding for Surfacing Under-served Music Content. 2765–2772. 4 indexed citations

6.

Bütepage, Judith, et al.. (2019). Modeling assumptions and evaluation schemes: On the assessment of deep latent variable models. Computer Vision and Pattern Recognition. 9–12. 1 indexed citations

7.

Bütepage, Judith, Hedvig Kjellström, & Danica Kragić. (2019). Predicting the What and How - a Probabilistic Semi-Supervised Approach to Multi-Task Human Activity Modeling. 2923–2926. 5 indexed citations

8.

Zhang, Cheng, Judith Bütepage, Hedvig Kjellström, & Stephan Mandt. (2018). Advances in Variational Inference. IEEE Transactions on Pattern Analysis and Machine Intelligence. 41(8). 2008–2026. 349 indexed citations breakdown →

9.

Bütepage, Judith, Hedvig Kjellström, & Danica Kragić. (2018). Anticipating Many Futures: Online Human Motion Prediction and Generation for Human-Robot Interaction. 4563–4570. 62 indexed citations

10.

Pokorny, Florian T., et al.. (2017). A database for reproducible manipulation research: CapriDB – Capture, Print, Innovate. Data in Brief. 11. 491–498.

11.

Bütepage, Judith, Michael J. Black, Danica Kragić, & Hedvig Kjellström. (2017). Deep Representation Learning for Human Motion Prediction and Classification. 1591–1599. 255 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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