Daniel Yamins
- Cognitive Neuroscience top 0.5%
- Neural dynamics and brain function 23
- Face Recognition and Perception 22
- Visual perception and processing mechanisms 19
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- Visual Attention and Saliency Detection 13
- Multimodal Machine Learning Applications 6
- Human Pose and Action Recognition 4
- Artificial Intelligence top 1%
- Domain Adaptation and Few-Shot Learning 4
- Biophysics top 1%
- Signal Processing top 5%
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- Modular Robots and Swarm Intelligence 4
- Co-authors
- James J. DiCarloDavid CoxJames BergstraHa HongCharles F. CadieuEthan A. SolomonChengxu ZhuangNajib J. Majaj
- Partner nations
- United StatesChinaBelgium
In The Last Decade
Daniel Yamins
60 papers receiving 4.6k citations
Hit Papers
Peers
Comparison fields: 5 of 179
- Cognitive Neuroscience 2.6k
- Computer Vision and Pattern Recognition 1.2k
- Artificial Intelligence 1.1k
- Biophysics 203
- Signal Processing 162
Countries citing papers authored by Daniel Yamins
This map shows the geographic impact of Daniel Yamins'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 Daniel Yamins with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Yamins more than expected).
Fields of papers citing papers by Daniel Yamins
This network shows the impact of papers produced by Daniel Yamins. 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 Daniel Yamins. The network helps show where Daniel Yamins may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Daniel Yamins, 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 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 14 | |
| 4 | 2024 | 7 | |
| 5 | 2023 | 11 | |
| 6 | 2021 | 169 | |
| 7 | Conditional Negative Sampling for Contrastive Learning of Visual Representations | 2021 | 3 |
| 8 | 2020 | 31 | |
| 9 | Two Routes to Scalable Credit Assignment without Weight Symmetry | 2020 | 2 |
| 10 | 2020 | 9 | |
| 11 | 2018 | 37 | |
| 12 | Task-driven convolutional recurrent models of the visual system | 2018 | 16 |
| 13 | Flexible neural representation for physics prediction | 2018 | 35 |
| 14 | 2016 | 5 | |
| 15 | Using goal-driven deep learning models to understand sensory cortexbreakdown → | 2016 | 822 |
| 16 | 2014 | 15 | |
| 17 | Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream | 2013 | 60 |
| 18 | Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architecturesbreakdown → | 2013 | 935 |
| 19 | 2008 | 0 | |
| 20 | 2008 | 22 |
About Daniel Yamins
Daniel Yamins is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 63 papers that have together received 4.7k indexed citations. Recurring topics across this work include Neural dynamics and brain function (23 papers), Face Recognition and Perception (22 papers), Visual perception and processing mechanisms (19 papers), Visual Attention and Saliency Detection (13 papers), Multimodal Machine Learning Applications (6 papers), Domain Adaptation and Few-Shot Learning (4 papers), Modular Robots and Swarm Intelligence (4 papers) and Human Pose and Action Recognition (4 papers). The work is most often cited by research in Cognitive Neuroscience (2.6k citations), Computer Vision and Pattern Recognition (1.2k citations) and Artificial Intelligence (1.1k citations). Daniel Yamins has collaborated with scholars based in United States, China and Belgium. Frequent co-authors include James J. DiCarlo, David Cox, James Bergstra, Ha Hong, Charles F. Cadieu, Ethan A. Solomon, Chengxu Zhuang, Najib J. Majaj, Alex Zhai and Josh H. McDermott. Their work appears in journals such as Journal of Vision, Neuron, Nature Neuroscience, Neural Computation and Cognitive Systems Research.
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.