Neural Pattern Synthesis
Neural Pattern Synthesis is a generative pattern design method that employs neural network architectures to algorithmically generate structured visual or spatial patterns through learned representations, distinguished by its reliance on deep learning models to synthesize novel, coherent patterns from implicit or explicit design principles.
20
Publications
1.7K
Citations
79
Authors
31
Institutions
Publications per year
2017–2026
8
Parents
20
1–5 of 20
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31

Cornell University
Ithaca, United States
2 h-index
6 publications
396 citations
University of California, Berkeley
Berkeley, United States
2 h-index
3 publications
811 citations

University of Freiburg
Freiburg im Breisgau, Germany
1 h-index
2 publications
512 citations
Weill Cornell Medicine
New York, United States
1 h-index
2 publications
56 citations
New York Institute of Technology
Vancouver, Canada
1 h-index
1 publications
17 citations
Resonance Research (United States)
Billerica, United States
1 h-index
5 publications
140 citations
University of Sheffield
Sheffield, United Kingdom
1 h-index
3 publications
162 citations
Pacific Biosciences (United States)
Menlo Park, United States
1 h-index
1 publications
19 citations
1–8 of 31
Repository · 3 publications · 313 citations
3
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3 publications · 463 citations
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1 publications · 28 citations
1
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QAM · 1 publications · 11 citations
1
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1 publications · 81 citations
1
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1–5