R Devon Hjelm

4.9k total citations
14 papers, 484 citations indexed

About

R Devon Hjelm is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, R Devon Hjelm has authored 14 papers receiving a total of 484 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 4 papers in Cognitive Neuroscience. Recurrent topics in R Devon Hjelm's work include Functional Brain Connectivity Studies (4 papers), Multimodal Machine Learning Applications (4 papers) and Neural dynamics and brain function (3 papers). R Devon Hjelm is often cited by papers focused on Functional Brain Connectivity Studies (4 papers), Multimodal Machine Learning Applications (4 papers) and Neural dynamics and brain function (3 papers). R Devon Hjelm collaborates with scholars based in United States, Canada and United Kingdom. R Devon Hjelm's co-authors include Philip Bachman, Vince D. Calhoun, Sergey Plis, Yoshua Bengio, Píetro Lió, Petar Veličković, William L. Hamilton, William Fedus, Ruslan Salakhutdinov and Tülay Adalı and has published in prestigious journals such as NeuroImage, IEEE Transactions on Medical Imaging and Frontiers in Neuroscience.

In The Last Decade

R Devon Hjelm

13 papers receiving 460 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
R Devon Hjelm United States 8 308 193 94 63 39 14 484
Junbo Ma China 9 187 0.6× 112 0.6× 60 0.6× 30 0.5× 34 0.9× 18 328
Helena Aidos Portugal 9 166 0.5× 188 1.0× 31 0.3× 36 0.6× 12 0.3× 27 417
Erkun Yang China 16 476 1.5× 1.0k 5.3× 144 1.5× 105 1.7× 44 1.1× 31 1.4k
Yingle Fan China 10 137 0.4× 155 0.8× 91 1.0× 75 1.2× 14 0.4× 48 364
Yuxuan Wei China 4 183 0.6× 88 0.5× 35 0.4× 10 0.2× 73 1.9× 7 313
P. S. Sathidevi India 12 203 0.7× 219 1.1× 142 1.5× 70 1.1× 8 0.2× 71 594
Kaifa Zhao China 9 155 0.5× 133 0.7× 36 0.4× 85 1.3× 8 0.2× 19 384
N.B. Karayiannis United States 8 191 0.6× 217 1.1× 47 0.5× 51 0.8× 8 0.2× 18 368
Alessandro Achille United States 8 223 0.7× 148 0.8× 25 0.3× 14 0.2× 16 0.4× 23 366

Countries citing papers authored by R Devon Hjelm

Since Specialization
Citations

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

Fields of papers citing papers by R Devon Hjelm

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by R Devon Hjelm. 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 R Devon Hjelm. The network helps show where R Devon Hjelm may publish in the future.

Co-authorship network of co-authors of R Devon Hjelm

This figure shows the co-authorship network connecting the top 25 collaborators of R Devon Hjelm. A scholar is included among the top collaborators of R Devon Hjelm 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 R Devon Hjelm. R Devon Hjelm 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.
Fedorov, Alex, Lei Wu, Thomas P. DeRamus, et al.. (2023). Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links. NeuroImage. 285. 120485–120485. 6 indexed citations
2.
Chuang, Ching-Yao, R Devon Hjelm, Xin Wang, et al.. (2022). Robust Contrastive Learning against Noisy Views. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 16649–16660. 49 indexed citations
3.
Reddy, Siva, et al.. (2021). Understanding by Understanding Not: Modeling Negation in Language Models. 1301–1312. 30 indexed citations
4.
Bachman, Philip, et al.. (2019). Learning Representations by Maximizing Mutual Information Across Views. Neural Information Processing Systems. 32. 15509–15519. 122 indexed citations
5.
Beckham, Christopher, Sina Honari, Alex Lamb, et al.. (2019). Adversarial Mixup Resynthesizers. PolyPublie (École Polytechnique de Montréal). 9 indexed citations
6.
Beckham, Christopher, Sina Honari, Vikas Verma, et al.. (2019). On Adversarial Mixup Resynthesis. PolyPublie (École Polytechnique de Montréal). 32. 4346–4357. 5 indexed citations
7.
El-Nouby, Alaaeldin, Shikhar Sharma, Hannes Schulz, et al.. (2018). Keep Drawing It: Iterative language-based image generation and editing.. arXiv (Cornell University). 4 indexed citations
8.
Hjelm, R Devon, et al.. (2018). Boundary Seeking GANs. International Conference on Learning Representations. 2 indexed citations
9.
Hjelm, R Devon, Eswar Damaraju, Kyunghyun Cho, et al.. (2018). Spatio-Temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks. Frontiers in Neuroscience. 12. 600–600. 11 indexed citations
10.
Veličković, Petar, William Fedus, William L. Hamilton, et al.. (2018). Deep Graph Infomax. Apollo (University of Cambridge). 118 indexed citations
11.
Bińkowski, Mikołaj, et al.. (2018). Unsupervised one-to-many image translation. 1 indexed citations
12.
Castro, Eduardo, R Devon Hjelm, Sergey Plis, et al.. (2016). Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia. IEEE Transactions on Medical Imaging. 35(7). 1729–1740. 21 indexed citations
13.
Hjelm, R Devon, Vince D. Calhoun, Ruslan Salakhutdinov, et al.. (2014). Restricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks. NeuroImage. 96. 245–260. 105 indexed citations
14.
Plis, Sergey, R Devon Hjelm, Ruslan Salakhutdinov, et al.. (2014). Deep learning models for brain imaging: model depth enhances discovery power. 5. 1 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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