Daniel Holtmann-Rice

450 citations
16 papers · 182 · h-index 8

Impact in

Papers in

Daniel Holtmann-Rice

16 papers receiving 173 citations

Peers

Daniel Holtmann-Rice
Comparison fields: 5 of 37
  • Computer Graphics and Computer-Aided Design 19
  • Computer Vision and Pattern Recognition 79
  • Cognitive Neuroscience 64
  • Computational Mathematics 2
  • Artificial Intelligence 66
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Citations per field
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Citations per year

Countries citing papers authored by Daniel Holtmann-Rice

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Holtmann-Rice

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Holtmann-Rice. 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 Holtmann-Rice. The network helps show where Daniel Holtmann-Rice may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Holtmann-Rice, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Holtmann-Rice Line = papers co-authored together Daniel Holtmann-Rice links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 201147
2 201629
3
Multiscale Quantization for Fast Similarity Search
201727
4 201819
5
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces
201916
6 201811
7 20189
8 20098
9
The Sparse Recovery Autoencoder.
20183
10
Loss Decomposition for Fast Learning in Large Output Spaces.
20183
11 20113
12 20132
13 20252
14
What's In A Patch, II: Visualizing generic surfaces.
20171
15 20131
16 20121

About Daniel Holtmann-Rice

Daniel Holtmann-Rice is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Computational Mechanics and Atomic and Molecular Physics, and Optics, having authored 16 papers that have together received 182 indexed citations. Recurring topics across this work include Visual perception and processing mechanisms (8 papers), Computer Graphics and Visualization Techniques (5 papers), Color Science and Applications (3 papers), Neural dynamics and brain function (3 papers), Color perception and design (2 papers), Sparse and Compressive Sensing Techniques (2 papers), 3D Shape Modeling and Analysis (2 papers) and Advanced Image and Video Retrieval Techniques (2 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (19 citations), Computer Vision and Pattern Recognition (79 citations), Cognitive Neuroscience (64 citations), Computational Mathematics (2 citations) and Artificial Intelligence (66 citations). Daniel Holtmann-Rice has collaborated with scholars based in United States, Germany and Israel. Frequent co-authors include Roland W. Fleming, HH Bülthoff, Sanjiv Kumar, Felix X. Yu, Ananda Theertha Suresh, Steven W. Zucker, Krzysztof Choromański, Xiang Wu, Satyen Kale and Ruiqi Guo. Their work appears in journals such as Journal of Vision, Proceedings of the National Academy of Sciences, Interface Focus, Journal of Mathematical Imaging and Vision and Journal of Physiology-Paris.

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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