Heuristic rank selection with progressively searching tensor ring network Article Swipe
Nannan Li
,
Yu Pan
,
Yaran Chen
,
Zixiang Ding
,
Dongbin Zhao
,
Zenglin Xu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1007/s40747-021-00308-x
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1007/s40747-021-00308-x
Recently, tensor ring networks (TRNs) have been applied in deep networks, achieving remarkable successes in compression ratio and accuracy. Although highly related to the performance of TRNs, rank selection is seldom studied in previous works and usually set to equal in experiments. Meanwhile, there is not any heuristic method to choose the rank, and an enumerating way to find appropriate rank is extremely time-consuming. Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region. Therefore, based on the above phenomenon, we propose a novel progressive genetic algorithm named progressively searching tensor ring network search (PSTRN), which has the ability to find optimal rank precisely and efficiently. Through the evolutionary phase and progressive phase, PSTRN can converge to the interest region quickly and harvest good performance. Experimental results show that PSTRN can significantly reduce the complexity of seeking rank, compared with the enumerating method. Furthermore, our method is validated on public benchmarks like MNIST, CIFAR10/100, UCF11 and HMDB51, achieving the state-of-the-art performance.
Related Topics
Concepts
MNIST database
Rank (graph theory)
Heuristic
Tensor (intrinsic definition)
Selection (genetic algorithm)
Set (abstract data type)
Computer science
Aggregate (composite)
Ring (chemistry)
Computational intelligence
Artificial intelligence
Algorithm
Mathematics
Machine learning
Mathematical optimization
Artificial neural network
Combinatorics
Geometry
Programming language
Chemistry
Materials science
Organic chemistry
Composite material
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s40747-021-00308-x
- https://link.springer.com/content/pdf/10.1007/s40747-021-00308-x.pdf
- OA Status
- gold
- Cited By
- 41
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3138562265
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3138562265Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s40747-021-00308-xDigital Object Identifier
- Title
-
Heuristic rank selection with progressively searching tensor ring networkWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-17Full publication date if available
- Authors
-
Nannan Li, Yu Pan, Yaran Chen, Zixiang Ding, Dongbin Zhao, Zenglin XuList of authors in order
- Landing page
-
https://doi.org/10.1007/s40747-021-00308-xPublisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s40747-021-00308-x.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s40747-021-00308-x.pdfDirect OA link when available
- Concepts
-
MNIST database, Rank (graph theory), Heuristic, Tensor (intrinsic definition), Selection (genetic algorithm), Set (abstract data type), Computer science, Aggregate (composite), Ring (chemistry), Computational intelligence, Artificial intelligence, Algorithm, Mathematics, Machine learning, Mathematical optimization, Artificial neural network, Combinatorics, Geometry, Programming language, Chemistry, Materials science, Organic chemistry, Composite materialTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
41Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 14, 2023: 11, 2022: 4, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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