Neil Houlsby

25 papers receiving 1.3k citations

Hit Papers

Scaling Vision Transformers20222026202320242022100200300400

Peers

Neil Houlsby
Comparison fields: 5 of 115
  • Artificial Intelligence 729
  • Computer Vision and Pattern Recognition 645
  • Atomic and Molecular Physics, and Optics 126
  • Information Systems 119
  • Radiology, Nuclear Medicine and Imaging 73
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Countries citing papers authored by Neil Houlsby

Since Specialization
Citations

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

Fields of papers citing papers by Neil Houlsby

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Neil Houlsby

This figure shows the co-authorship network connecting the top 25 collaborators of Neil Houlsby. A scholar is included among the top collaborators of Neil Houlsby 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 Neil Houlsby. Neil Houlsby 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
#WorkIndexed citations
1 3
2 17
3
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
143
4
A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches
2
5
Automatic Shortcut Removal for Self-Supervised Representation Learning
2
6
Large Scale Learning of General Visual Representations for Transfer.
26
7
The Visual Task Adaptation Benchmark
22
8 182
9
On Self Modulation for Generative Adversarial Networks
14
10
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
40
11
Transfer Automatic Machine Learning.
2
12
Cold-start Active Learning with Robust Ordinal Matrix Factorization
30
13
Probabilistic Matrix Factorization with Non-random Missing Data
75
14
Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices
15
15
A Filtering Approach to Stochastic Variational Inference
1
16 39
17 55
18 33
19
Collaborative Gaussian Processes for Preference Learning
45
20 105

About Neil Houlsby

Neil Houlsby is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics, having authored 25 papers that have together received 1.3k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (9 papers), Advanced Neural Network Applications (6 papers) and Machine Learning and Algorithms (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (645 citations), Artificial Intelligence (729 citations) and Signal Processing (72 citations). Neil Houlsby has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Xiaohua Zhai, Lucas Beyer, Alexander Kolesnikov, Ferenc Huszár, Zoubin Ghahramani, José Miguel Hernández-Lobato, Mario Lučić, Ting Chen, Marvin Ritter and Sylvain Gelly. Their work appears in journals such as Current Biology, Physical Review A and Géotechnique.

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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