Jenia Jitsev

2.5k total citations · 1 hit paper
23 papers, 453 citations indexed

About

Jenia Jitsev is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Jenia Jitsev has authored 23 papers receiving a total of 453 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Electrical and Electronic Engineering, 6 papers in Computer Vision and Pattern Recognition and 6 papers in Cognitive Neuroscience. Recurrent topics in Jenia Jitsev's work include Neural dynamics and brain function (6 papers), Solar Thermal and Photovoltaic Systems (4 papers) and Solar Radiation and Photovoltaics (3 papers). Jenia Jitsev is often cited by papers focused on Neural dynamics and brain function (6 papers), Solar Thermal and Photovoltaic Systems (4 papers) and Solar Radiation and Photovoltaics (3 papers). Jenia Jitsev collaborates with scholars based in Germany, United States and Japan. Jenia Jitsev's co-authors include Mehdi Cherti, Mitchell Wortsman, Gabriel Ilharco, Christoph Schuhmann, Ludwig Schmidt, Romain Beaumont, Ross Wightman, Mathis Bode, Michael Gauding and Heinz Pitsch and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nanoscale and Solar Energy.

In The Last Decade

Jenia Jitsev

22 papers receiving 433 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.

Reproducible Scaling Laws for Contrastive Language-Image Learning

2023 213 citations Mehdi Cherti, Romain Beaumont et al. profile →

Peers

Jenia Jitsev

CVPR

AI

CM

AE

EEE

Boxi Wu China

Jan Eric Lenssen Germany

Hayaru Shouno Japan

Haishun Du China

Wei Pan China

Allan Martins Brazil

Luca Cosmo Italy

Florian Strub United States

Sara Sabour United States

Boxi Wu China

Jenia Jitsev

232 ×1.2

CVPR

147 ×1.1

AI

34 ×0.5

CM

45 ×1.5

AE

57 ×1.9

EEE

Citations per year, relative to Jenia Jitsev Jenia Jitsev (= 1×) peers Boxi Wu

Countries citing papers authored by Jenia Jitsev

Since Specialization

Citations

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

Fields of papers citing papers by Jenia Jitsev

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jenia Jitsev

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Cherti, Mehdi, et al.. (2025). Scalable heliostat surface predictions from focal spots: Sim-to-Real transfer of inverse Deep Learning Raytracing. Solar Energy. 300. 113726–113726. 1 indexed citations

2.

Cherti, Mehdi, et al.. (2025). Inverse Deep Learning Raytracing for heliostat surface prediction. Solar Energy. 289. 113312–113312. 3 indexed citations

3.

Cherti, Mehdi, et al.. (2024). Flux density distribution forecasting in concentrated solar tower plants: A data-driven approach. Solar Energy. 282. 112894–112894. 3 indexed citations

4.

Cherti, Mehdi, et al.. (2024). In-situ UNet-based heliostat beam characterization method for precise flux calculation using the camera-target method. Solar Energy. 279. 112811–112811. 3 indexed citations

5.

Cherti, Mehdi, Romain Beaumont, Ross Wightman, et al.. (2023). Reproducible Scaling Laws for Contrastive Language-Image Learning. 2818–2829. 213 indexed citations breakdown →

6.

Cherti, Mehdi & Jenia Jitsev. (2022). Effect of pre-training scale on intra- and inter-domain, full and few-shot transfer learning for natural and X-Ray chest images. 2022 International Joint Conference on Neural Networks (IJCNN). 1–9. 14 indexed citations

7.

Batool, Mariah, Jasna Janković, Jenia Jitsev, et al.. (2021). Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells. Nanoscale. 14(1). 10–18. 23 indexed citations

8.

Bode, Mathis, et al.. (2021). Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows. Proceedings of the Combustion Institute. 38(2). 2617–2625. 90 indexed citations

9.

Columbie, J. M. Clavijo, P. C. F. Glaysher, Jenia Jitsev, & J. Katzy. (2021). Adversarial domain adaptation to reduce sample bias of a high energy physics event classifier ^*. Machine Learning Science and Technology. 3(1). 15014–15014. 8 indexed citations

10.

Brenzke, M., S. Wiesen, M. Bernert, et al.. (2021). Divertor power load predictions based on machine learning. Nuclear Fusion. 61(4). 46023–46023. 2 indexed citations

11.

Eslamibidgoli, Mohammad J., et al.. (2021). Convolutional neural networks for high throughput screening of catalyst layer inks for polymer electrolyte fuel cells. RSC Advances. 11(51). 32126–32134. 9 indexed citations

12.

Zhang, Run, Gabriele Cavallaro, & Jenia Jitsev. (2020). Super-Resolution of Large Volumes of Sentinel-2 Images with High Performance Distributed Deep Learning. 11 indexed citations

13.

Krause, Dorian, Bernd Mohr, Wolfgang Frings, et al.. (2020). Developing Exascale Computing at JSC. 2 indexed citations

14.

Cavallaro, Gabriele, et al.. (2020). Scaling Up a Multispectral Resnet-50 to 128 GPUs. abs 1706 2677. 1058–1061. 6 indexed citations

15.

Cavallaro, Gabriele, et al.. (2019). Remote Sensing Big Data Classification with High Performance Distributed Deep Learning. Remote Sensing. 11(24). 3056–3056. 31 indexed citations

16.

Morita, Kenji, Jenia Jitsev, & Abigail Morrison. (2016). Corticostriatal circuit mechanisms of value-based action selection: Implementation of reinforcement learning algorithms and beyond. Behavioural Brain Research. 311. 110–121. 10 indexed citations

17.

Duarte, Renato, et al.. (2015). ROS-MUSIC toolchain for spiking neural network simulations in a robotic environment. BMC Neuroscience. 16(S1). 1 indexed citations

18.

Jitsev, Jenia, Abigail Morrison, & Marc Tittgemeyer. (2012). Learning from positive and negative rewards in a spiking neural network model of basal ganglia. 1–8. 8 indexed citations

19.

Jitsev, Jenia & Christoph von der Malsburg. (2010). Off-line memory reprocessing following on-line unsupervised learning strongly improves recognition performance in a hierarchical visual memory. 304. 1–8. 1 indexed citations

20.

Jitsev, Jenia. (2009). Experience-driven formation of parts-based representations in a model of layered visual memory. Frontiers in Computational Neuroscience. 3. 15–15. 5 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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