Tijmen Blankevoort

1.0k total citations
14 papers, 227 citations indexed

About

Tijmen Blankevoort is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Statistical and Nonlinear Physics. According to data from OpenAlex, Tijmen Blankevoort has authored 14 papers receiving a total of 227 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 12 papers in Artificial Intelligence and 2 papers in Statistical and Nonlinear Physics. Recurrent topics in Tijmen Blankevoort's work include Advanced Neural Network Applications (12 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Adversarial Robustness in Machine Learning (5 papers). Tijmen Blankevoort is often cited by papers focused on Advanced Neural Network Applications (12 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Adversarial Robustness in Machine Learning (5 papers). Tijmen Blankevoort collaborates with scholars based in United Kingdom, Netherlands and Switzerland. Tijmen Blankevoort's co-authors include Markus Nagel, Nojun Kwak, Yash Bhalgat, Jinwon Lee, Dushyant Mehta, Babak Ehteshami Bejnordi, Max Welling, Giovanni Mariani, Bert Moons and Amirhossein Habibian and has published in prestigious journals such as 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul National University Open Repository (Seoul National University) and UvA-DARE (University of Amsterdam).

In The Last Decade

Tijmen Blankevoort

14 papers receiving 216 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Tijmen Blankevoort United Kingdom 7 143 126 36 15 15 14 227
Elad Hoffer Israel 5 183 1.3× 152 1.2× 43 1.2× 9 0.6× 8 0.5× 10 256
Yury Nahshan Israel 3 185 1.3× 127 1.0× 60 1.7× 17 1.1× 9 0.6× 3 235
Ruizhou Ding United States 8 159 1.1× 142 1.1× 49 1.4× 19 1.3× 7 0.5× 12 231
Ting-Wu Chin United States 8 144 1.0× 121 1.0× 45 1.3× 41 2.7× 33 2.2× 13 226
Zheng Zhan United States 7 83 0.6× 66 0.5× 43 1.2× 28 1.9× 13 0.9× 22 167
Wenshuo Li China 7 129 0.9× 113 0.9× 114 3.2× 15 1.0× 9 0.6× 10 222
Chenqian Yan United Kingdom 4 309 2.2× 264 2.1× 27 0.8× 11 0.7× 6 0.4× 4 376
Jinheng Xie China 7 259 1.8× 214 1.7× 15 0.4× 8 0.5× 22 1.5× 16 381
Tianjun Xiao China 7 138 1.0× 147 1.2× 8 0.2× 13 0.9× 6 0.4× 13 235

Countries citing papers authored by Tijmen Blankevoort

Since Specialization
Citations

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

Fields of papers citing papers by Tijmen Blankevoort

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tijmen Blankevoort

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

All Works

14 of 14 papers shown
1.
Nagel, Markus, et al.. (2023). QBitOpt: Fast and Accurate Bitwidth Reallocation during Training. 1274–1283. 3 indexed citations
2.
Blankevoort, Tijmen, et al.. (2023). Efficient Neural PDE-Solvers using Quantization Aware Training. UvA-DARE (University of Amsterdam). 1415–1424. 1 indexed citations
3.
Havtorn, Jakob D., et al.. (2023). MSViT: Dynamic Mixed-scale Tokenization for Vision Transformers. 838–848. 5 indexed citations
4.
Srinivas, Suraj, et al.. (2022). Cyclical Pruning for Sparse Neural Networks. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2761–2770. 13 indexed citations
5.
Nagel, Markus, et al.. (2022). Simulated Quantization, Real Power Savings. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2756–2760. 8 indexed citations
6.
Mehta, Dushyant, et al.. (2022). Simple and Efficient Architectures for Semantic Segmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2627–2635. 15 indexed citations
7.
Nagel, Markus, et al.. (2021). Understanding and Overcoming the Challenges of Efficient Transformer Quantization. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 7947–7969. 41 indexed citations
8.
Moons, Bert, et al.. (2021). Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 12209–12218. 20 indexed citations
9.
Alizadeh, Milad, et al.. (2020). Gradient 𝓁 1 Regularization for Quantization Robustness.. arXiv (Cornell University). 2 indexed citations
10.
Bejnordi, Babak Ehteshami, Tijmen Blankevoort, & Max Welling. (2020). Batch-shaping for learning conditional channel gated networks. International Conference on Learning Representations. 6 indexed citations
11.
Louizos, Christos, Markus Nagel, Rana Ali Amjad, et al.. (2020). Bayesian Bits: Unifying Quantization and Pruning. arXiv (Cornell University). 33. 5741–5752. 1 indexed citations
12.
Bhalgat, Yash, Jinwon Lee, Markus Nagel, Tijmen Blankevoort, & Nojun Kwak. (2020). LSQ+: Improving low-bit quantization through learnable offsets and better initialization. Seoul National University Open Repository (Seoul National University). 2978–2985. 104 indexed citations
13.
Alizadeh, Milad, et al.. (2020). Gradient $\ell_1$ Regularization for Quantization Robustness. arXiv (Cornell University). 4 indexed citations
14.
Bejnordi, Babak Ehteshami, Tijmen Blankevoort, & Max Welling. (2019). Batch-Shaped Channel Gated Networks.. arXiv (Cornell University). 4 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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