Adrian Bulat

3.2k citations
25 papers · 923 · h-index 15

Impact in

Papers in

Adrian Bulat

22 papers receiving 905 citations

Peers

Adrian Bulat
Comparison fields: 5 of 102
  • Computer Vision and Pattern Recognition 517
  • Signal Processing 104
  • Experimental and Cognitive Psychology 83
  • Artificial Intelligence 179
  • Plant Science 195
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Citations per year

Countries citing papers authored by Adrian Bulat

Since Specialization
Citations

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

Fields of papers citing papers by Adrian Bulat

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Adrian Bulat, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Adrian Bulat Line = papers co-authored together Adrian Bulat links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017246
2 2017243
3 2021127
4 201640
5 202037
6
Knowledge distillation via softmax regression representation learning
202132
7 201828
8 202324
9 202323
10
XNOR-Net++: Improved binary neural networks.
201919
11 202018
12 202117
13
High-Capacity Expert Binary Networks
202116
14 202014
15 202314
16 202312
17 20244
18 20233
19
Efficient N-Dimensional Convolutions via Higher-Order Factorization
20192
20 20242

About Adrian Bulat

Adrian Bulat is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Computational Mathematics and Cognitive Neuroscience, having authored 25 papers that have together received 923 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (10 papers), Multimodal Machine Learning Applications (9 papers), Advanced Neural Network Applications (8 papers), Face recognition and analysis (6 papers), Face and Expression Recognition (3 papers), Biometric Identification and Security (2 papers), Tensor decomposition and applications (2 papers) and Human Pose and Action Recognition (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (517 citations), Signal Processing (104 citations), Experimental and Cognitive Psychology (83 citations), Artificial Intelligence (179 citations) and Plant Science (195 citations). Adrian Bulat has collaborated with scholars based in United Kingdom, South Korea and United States. Frequent co-authors include Georgios Tzimiropoulos, Aaron S. Jackson, Vasileios Argyriou, Brais Martínez, Maja Pantić, Jean Kossaifi, Antoine Toisoul, Michael P. Pound, Andrew P. French and Marcus Griffiths. Their work appears in journals such as International Journal of Computer Vision, Nature Machine Intelligence, GigaScience, IEEE Transactions on Pattern Analysis and Machine Intelligence and Repository@Nottingham (University of Nottingham).

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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