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.
Performance-optimized hierarchical models predict neural responses in higher visual cortex
2014997 citationsDaniel Yamins, Ha Hong et al.Proceedings of the National Academy of Sciencesprofile →
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).
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 of co-authors of Daniel Yamins
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Yamins.
A scholar is included among the top collaborators of Daniel Yamins 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 Daniel Yamins. Daniel Yamins is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wu, Mike, et al.. (2021). Conditional Negative Sampling for Contrastive Learning of Visual Representations. arXiv (Cornell University).3 indexed citations
8.
Kunin, Daniel, et al.. (2020). Two Routes to Scalable Credit Assignment without Weight Symmetry. International Conference on Machine Learning. 1. 5511–5521.2 indexed citations
Nayebi, Aran, Daniel M. Bear, Jonas Kubilius, et al.. (2018). Task-driven convolutional recurrent models of the visual system. Lirias (KU Leuven). 31. 5290–5301.16 indexed citations
12.
Mrowca, Damian, Chengxu Zhuang, Nick Haber, et al.. (2018). Flexible neural representation for physics prediction. DSpace@MIT (Massachusetts Institute of Technology). 31. 8799–8810.35 indexed citations
Yamins, Daniel & James J. DiCarlo. (2016). Using goal-driven deep learning models to understand sensory cortex. Nature Neuroscience. 19(3). 356–365.822 indexed citations breakdown →
15.
Fan, Judith E., Daniel Yamins, & Nicholas B. Turk‐Browne. (2015). Common object representations for visual recognition and production.. Cognitive Science.4 indexed citations
Yamins, Daniel, Ha Hong, Charles F. Cadieu, & James J. DiCarlo. (2013). Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream. DSpace@MIT (Massachusetts Institute of Technology). 26. 3093–3101.60 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.