Evan Archer
Impact in
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- Neural dynamics and brain function
- Visual perception and processing mechanisms
- EEG and Brain-Computer Interfaces
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- Statistical Mechanics and Entropy
Papers in
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- Neural dynamics and brain function 5
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- Bayesian Methods and Mixture Models 3
- Bayesian Modeling and Causal Inference 2
- Neural Networks and Applications 2
- Co-authors
- Jonathan W. Pillow (7 shared papers)Il Memming Park (6 shared papers)Nicholas J. Priebe (1 shared paper)Jakob H. Macke (2 shared papers)Urs Köster (1 shared paper)Srinivas C. Turaga (1 shared paper)Lars Buesing (1 shared paper)Artur Speiser (1 shared paper)
- Journals
- Journal of Machine Learning Research (1 paper)Entropy (1 paper)Max Planck Digital Library (1 paper)International Conference on Artificial Intelligence and Statistics (1 paper)Neural Information Processing Systems (4 papers)
- Partner nations
- United StatesGermany
In The Last Decade
Evan Archer
8 papers receiving 166 citations
Peers
Comparison fields: 5 of 60
- Cognitive Neuroscience 78
- Statistical and Nonlinear Physics 31
- Statistics and Probability 14
- Artificial Intelligence 55
- Cellular and Molecular Neuroscience 28
Countries citing papers authored by Evan Archer
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
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-authors
The 17 scholars most cited alongside Evan Archer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 41 | |
| 2 | 2013 | 36 | |
| 3 | Spectral methods for neural characterization using generalized quadratic models | 2013 | 33 |
| 4 | Low-dimensional models of neural population activity in sensory cortical circuits | 2014 | 20 |
| 5 | Bayesian entropy estimation for binary spike train data using parametric prior knowledge | 2013 | 16 |
| 6 | Bayesian estimation of discrete entropy with mixtures of stick-breaking priors | 2012 | 10 |
| 7 | Fast amortized inference of neural activity from calcium imaging data with variational autoencoders | 2017 | 6 |
| 8 | Universal models for binary spike patterns using centered Dirichlet processes | 2013 | 4 |
| 9 | Scalable Variational Inference for Super Resolution Microscopy | 2017 | 0 |
| 10 | 2022 | 0 |
About Evan Archer
Evan Archer is a scholar working on Cognitive Neuroscience, Artificial Intelligence, Biophysics, Signal Processing and Statistics and Probability, having authored 10 papers that have together received 166 indexed citations. Recurring topics across this work include Neural dynamics and brain function (5 papers), Bayesian Methods and Mixture Models (3 papers), Bayesian Modeling and Causal Inference (2 papers), Neural Networks and Applications (2 papers), Blind Source Separation Techniques (2 papers), Advanced Fluorescence Microscopy Techniques (2 papers), Statistical Methods and Inference (2 papers) and Statistical Mechanics and Entropy (1 paper). The work is most often cited by research in Cognitive Neuroscience (78 citations), Statistical and Nonlinear Physics (31 citations), Statistics and Probability (14 citations), Artificial Intelligence (55 citations) and Cellular and Molecular Neuroscience (28 citations). Evan Archer has collaborated with scholars based in United States and Germany. Frequent co-authors include Jonathan W. Pillow, Il Memming Park, Nicholas J. Priebe, Jakob H. Macke, Urs Köster, Srinivas C. Turaga, Lars Buesing, Artur Speiser, Jinyao Yan and Kenneth W. Latimer. Their work appears in journals such as Journal of Machine Learning Research, Entropy, Max Planck Digital Library, International Conference on Artificial Intelligence and Statistics and Neural Information Processing Systems.
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.