Ryan Prenger

6.7k total citations · 1 hit paper
16 papers, 1.6k citations indexed

About

Ryan Prenger is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ryan Prenger has authored 16 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 5 papers in Cognitive Neuroscience and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ryan Prenger's work include Neural dynamics and brain function (4 papers), Visual perception and processing mechanisms (3 papers) and Speech Recognition and Synthesis (3 papers). Ryan Prenger is often cited by papers focused on Neural dynamics and brain function (4 papers), Visual perception and processing mechanisms (3 papers) and Speech Recognition and Synthesis (3 papers). Ryan Prenger collaborates with scholars based in United States, Hong Kong and Italy. Ryan Prenger's co-authors include Jack L. Gallant, Kendrick Kay, Thomas Naselaris, Michael Oliver, Brian Van Essen, Maya Gokhale, Ben D. B. Willmore, Stephen V. David, Andrew Gibiansky and Adam Coates and has published in prestigious journals such as Nature, Neuron and Journal of Neuroscience.

In The Last Decade

Ryan Prenger

15 papers receiving 1.6k 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.

Identifying natural images from human brain activity

2008 835 citations Kendrick Kay, Thomas Naselaris et al. Nature profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ryan Prenger United States	7	1.2k	290	289	166	113	16	1.6k
Shinji Nishimoto Japan	16	2.1k 1.8×	255 0.9×	381 1.3×	101 0.6×	156 1.4×	50	2.6k
Okito Yamashita Japan	18	1.2k 1.0×	148 0.5×	126 0.4×	163 1.0×	384 3.4×	63	1.6k
Hatim Aboalsamh Saudi Arabia	20	272 0.2×	300 1.0×	567 2.0×	201 1.2×	145 1.3×	97	1.3k
Pietro Berkes United States	12	1.2k 1.0×	295 1.0×	118 0.4×	143 0.9×	18 0.2×	19	1.6k
Andrew Saxe United Kingdom	16	485 0.4×	561 1.9×	352 1.2×	84 0.5×	31 0.3×	39	1.3k
Yoichi Miyawaki Japan	10	730 0.6×	88 0.3×	130 0.4×	54 0.3×	44 0.4×	25	886
Umut Güçlü Netherlands	18	474 0.4×	132 0.5×	241 0.8×	46 0.3×	24 0.2×	42	844
Vicente L. Malave United States	7	1.2k 1.0×	285 1.0×	137 0.5×	55 0.3×	106 0.9×	7	1.6k
Odelia Schwartz United States	18	1.8k 1.5×	171 0.6×	246 0.9×	161 1.0×	17 0.2×	40	2.1k
Changde Du China	15	526 0.4×	234 0.8×	259 0.9×	88 0.5×	23 0.2×	47	884

Countries citing papers authored by Ryan Prenger

Since Specialization

Citations

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

Fields of papers citing papers by Ryan Prenger

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ryan Prenger

This figure shows the co-authorship network connecting the top 25 collaborators of Ryan Prenger. A scholar is included among the top collaborators of Ryan Prenger 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 Ryan Prenger. Ryan Prenger 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:

16 of 16 papers shown

1.

Su, Dan, Mostofa Patwary, Shrimai Prabhumoye, et al.. (2023). Context Generation Improves Open Domain Question Answering. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 793–808. 4 indexed citations

2.

Prenger, Ryan, et al.. (2023). LeanDojo: Theorem Proving with Retrieval-Augmented Language Models. 21573–21612.

3.

Xu, Peng, Mostofa Patwary, Shrimai Prabhumoye, et al.. (2022). Evaluating Parameter Efficient Learning for Generation. 4824–4833. 1 indexed citations

4.

Valle, Rafael, Kevin J. Shih, Ryan Prenger, & Bryan Catanzaro. (2021). Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis. International Conference on Learning Representations. 2 indexed citations

5.

Arık, Sercan Ö., Rewon Child, Joel Hestness, et al.. (2017). Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting. 1606–1610. 106 indexed citations

6.

Ni, Karl & Ryan Prenger. (2013). Learning features in deep architectures with unsupervised kernel k-means. 981–984. 2 indexed citations

7.

Essen, Brian Van, et al.. (2012). Accelerating a Random Forest Classifier: Multi-Core, GP-GPU, or FPGA?. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 232–239. 125 indexed citations

8.

Varshney, Kush R., et al.. (2012). Practical Ensemble Classification Error Bounds for Different Operating Points. IEEE Transactions on Knowledge and Data Engineering. 25(11). 2590–2601. 6 indexed citations

9.

Willmore, Ben D. B., Ryan Prenger, & Jack L. Gallant. (2010). Neural Representation of Natural Images in Visual Area V2. Journal of Neuroscience. 30(6). 2102–2114. 83 indexed citations

10.

Prenger, Ryan, et al.. (2010). Class-specific error bounds for ensemble classifiers. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 843–852. 3 indexed citations

11.

Naselaris, Thomas, Ryan Prenger, Kendrick Kay, Michael Oliver, & Jack L. Gallant. (2009). Bayesian Reconstruction of Natural Images from Human Brain Activity. Neuron. 63(6). 902–915. 343 indexed citations

12.

Kay, Kendrick, Thomas Naselaris, Ryan Prenger, & Jack L. Gallant. (2008). Identifying natural images from human brain activity. Nature. 452(7185). 352–355. 835 indexed citations breakdown →

13.

Kay, Kendrick, Stephen V. David, Ryan Prenger, Kathleen A. Hansen, & Jack L. Gallant. (2007). Modeling low‐frequency fluctuation and hemodynamic response timecourse in event‐related fMRI. Human Brain Mapping. 29(2). 142–156. 55 indexed citations

14.

Willmore, Ben D. B., Ryan Prenger, & Jack L. Gallant. (2005). Principles of neural shape coding in area V2. Journal of Vision. 5(8). 82–82. 2 indexed citations

15.

Kim, Yeong E., et al.. (2005). EXPERIMENTAL TEST OF BOSE–EINSTEIN CONDENSATION MECHANISM FOR LOW ENERGY NUCLEAR REACTION IN NANOSCALE ATOMIC CLUSTERS. 789–799. 1 indexed citations

16.

Prenger, Ryan, Michael C. Wu, Stephen V. David, & Jack L. Gallant. (2004). Nonlinear V1 responses to natural scenes revealed by neural network analysis. Neural Networks. 17(5-6). 663–679. 51 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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