Evan Archer

487 total citations
9 papers, 161 citations indexed

About

Evan Archer is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Biophysics. According to data from OpenAlex, Evan Archer has authored 9 papers receiving a total of 161 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Cognitive Neuroscience, 5 papers in Artificial Intelligence and 3 papers in Biophysics. Recurrent topics in Evan Archer's work include Neural dynamics and brain function (5 papers), Bayesian Methods and Mixture Models (3 papers) and Neural Networks and Applications (2 papers). Evan Archer is often cited by papers focused on Neural dynamics and brain function (5 papers), Bayesian Methods and Mixture Models (3 papers) and Neural Networks and Applications (2 papers). Evan Archer collaborates with scholars based in United States and Germany. Evan Archer's co-authors include Jonathan W. Pillow, Il Memming Park, Nicholas J. Priebe, Jakob H. Macke, Urs Köster, Lars Buesing, Jinyao Yan, Artur Speiser, Kenneth W. Latimer and Srinivas C. Turaga and has published in prestigious journals such as Journal of Machine Learning Research, Entropy and Neural Information Processing Systems.

In The Last Decade

Evan Archer

8 papers receiving 161 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Evan Archer United States 7 75 53 31 29 23 9 161
Kaan Öcal United Kingdom 5 82 1.1× 39 0.7× 20 0.6× 34 1.2× 67 2.9× 8 192
Jeremy Lewi United States 4 226 3.0× 60 1.1× 30 1.0× 110 3.8× 35 1.5× 6 281
Tatyana S. Turova Sweden 8 56 0.7× 38 0.7× 139 4.5× 16 0.6× 5 0.2× 36 239
Carlo Nicolini Italy 9 146 1.9× 34 0.6× 81 2.6× 16 0.6× 29 1.3× 14 269
Joshua Cape United States 7 46 0.6× 53 1.0× 58 1.9× 5 0.2× 18 0.8× 21 175
Niv Cohen Israel 5 47 0.6× 23 0.4× 31 1.0× 17 0.6× 65 2.8× 8 132
Mijung Park United States 6 95 1.3× 34 0.6× 4 0.1× 38 1.3× 20 0.9× 17 168
Maria K. Kurovskaya Russia 9 120 1.6× 37 0.7× 218 7.0× 16 0.6× 7 0.3× 19 327
Christian Uhl Germany 7 149 2.0× 33 0.6× 54 1.7× 11 0.4× 12 0.5× 13 216
E. J. Ngamga Germany 9 116 1.5× 25 0.5× 160 5.2× 25 0.9× 28 1.2× 10 305

Countries citing papers authored by Evan Archer

Since Specialization
Citations

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

Fields of papers citing papers by Evan Archer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Evan Archer

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

All Works

9 of 9 papers shown
1.
Speiser, Artur, Jinyao Yan, Evan Archer, et al.. (2017). Fast amortized inference of neural activity from calcium imaging data with variational autoencoders. MPG.PuRe (Max Planck Society). 30. 4024–4034. 6 indexed citations
2.
Sun, Ruoxi, Evan Archer, & Liam Paninski. (2017). Scalable Variational Inference for Super Resolution Microscopy. International Conference on Artificial Intelligence and Statistics. 1057–1065.
3.
Archer, Evan, Il Memming Park, & Jonathan W. Pillow. (2014). Bayesian entropy estimation for countable discrete distributions. Journal of Machine Learning Research. 15(1). 2833–2868. 39 indexed citations
4.
Archer, Evan, Urs Köster, Jonathan W. Pillow, & Jakob H. Macke. (2014). Low-dimensional models of neural population activity in sensory cortical circuits. Max Planck Digital Library. 27. 343–351. 20 indexed citations
5.
Park, Il Memming, Evan Archer, Kenneth W. Latimer, & Jonathan W. Pillow. (2013). Universal models for binary spike patterns using centered Dirichlet processes. Neural Information Processing Systems. 26. 2463–2471. 4 indexed citations
6.
Park, Il Memming, Evan Archer, Nicholas J. Priebe, & Jonathan W. Pillow. (2013). Spectral methods for neural characterization using generalized quadratic models. Neural Information Processing Systems. 26. 2454–2462. 32 indexed citations
7.
Archer, Evan, Il Memming Park, & Jonathan W. Pillow. (2013). Bayesian entropy estimation for binary spike train data using parametric prior knowledge. Neural Information Processing Systems. 26. 1700–1708. 15 indexed citations
8.
Archer, Evan, Il Memming Park, & Jonathan W. Pillow. (2013). Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data. Entropy. 15(5). 1738–1755. 35 indexed citations
9.
Archer, Evan, Il Memming Park, & Jonathan W. Pillow. (2012). Bayesian estimation of discrete entropy with mixtures of stick-breaking priors. Neural Information Processing Systems. 25. 2015–2023. 10 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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