NASGEM: Neural Architecture Search via Graph Embedding Method Article Swipe
Hsin-Pai Cheng
,
Tunhou Zhang
,
Yixing Zhang
,
Shiyu Li
,
Feng Liang
,
Feng Yan
,
Meng Li
,
Vikas Chandra
,
Hai Li
,
Yiran Chen
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.04452
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.04452
Neural Architecture Search (NAS) automates and prospers the design of neural networks. Estimator-based NAS has been proposed recently to model the relationship between architectures and their performance to enable scalable and flexible search. However, existing estimator-based methods encode the architecture into a latent space without considering graph similarity. Ignoring graph similarity in node-based search space may induce a large inconsistency between similar graphs and their distance in the continuous encoding space, leading to inaccurate encoding representation and/or reduced representation capacity that can yield sub-optimal search results. To preserve graph correlation information in encoding, we propose NASGEM which stands for Neural Architecture Search via Graph Embedding Method. NASGEM is driven by a novel graph embedding method equipped with similarity measures to capture the graph topology information. By precisely estimating the graph distance and using an auxiliary Weisfeiler-Lehman kernel to guide the encoding, NASGEM can utilize additional structural information to get more accurate graph representation to improve the search efficiency. GEMNet, a set of networks discovered by NASGEM, consistently outperforms networks crafted by existing search methods in classification tasks, i.e., with 0.4%-3.6% higher accuracy while having 11%- 21% fewer Multiply-Accumulates. We further transfer GEMNet for COCO object detection. In both one-stage and twostage detectors, our GEMNet surpasses its manually-crafted and automatically-searched counterparts.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2007.04452
- https://arxiv.org/pdf/2007.04452
- OA Status
- green
- Cited By
- 10
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3041166015
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https://openalex.org/W3041166015Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2007.04452Digital Object Identifier
- Title
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NASGEM: Neural Architecture Search via Graph Embedding MethodWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-07-08Full publication date if available
- Authors
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Hsin-Pai Cheng, Tunhou Zhang, Yixing Zhang, Shiyu Li, Feng Liang, Feng Yan, Meng Li, Vikas Chandra, Hai Li, Yiran ChenList of authors in order
- Landing page
-
https://arxiv.org/abs/2007.04452Publisher landing page
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https://arxiv.org/pdf/2007.04452Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2007.04452Direct OA link when available
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Computer science, Graph embedding, Embedding, Theoretical computer science, Scalability, Graph, ENCODE, Artificial intelligence, Gene, Database, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2023: 2, 2022: 1, 2021: 4, 2020: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.space | 43, 54 |
| abstract_inverted_index.their | 25, 64 |
| abstract_inverted_index.using | 132 |
| abstract_inverted_index.which | 96 |
| abstract_inverted_index.while | 182 |
| abstract_inverted_index.yield | 82 |
| abstract_inverted_index.GEMNet | 191, 203 |
| abstract_inverted_index.NASGEM | 95, 106, 141 |
| abstract_inverted_index.Neural | 0, 99 |
| abstract_inverted_index.Search | 2, 101 |
| abstract_inverted_index.and/or | 76 |
| abstract_inverted_index.design | 8 |
| abstract_inverted_index.driven | 108 |
| abstract_inverted_index.enable | 28 |
| abstract_inverted_index.encode | 37 |
| abstract_inverted_index.graphs | 62 |
| abstract_inverted_index.having | 183 |
| abstract_inverted_index.higher | 180 |
| abstract_inverted_index.induce | 56 |
| abstract_inverted_index.kernel | 136 |
| abstract_inverted_index.latent | 42 |
| abstract_inverted_index.method | 114 |
| abstract_inverted_index.neural | 10 |
| abstract_inverted_index.object | 194 |
| abstract_inverted_index.search | 53, 84, 156, 172 |
| abstract_inverted_index.space, | 70 |
| abstract_inverted_index.stands | 97 |
| abstract_inverted_index.tasks, | 176 |
| abstract_inverted_index.GEMNet, | 158 |
| abstract_inverted_index.Method. | 105 |
| abstract_inverted_index.NASGEM, | 165 |
| abstract_inverted_index.between | 22, 60 |
| abstract_inverted_index.capture | 120 |
| abstract_inverted_index.crafted | 169 |
| abstract_inverted_index.further | 189 |
| abstract_inverted_index.improve | 154 |
| abstract_inverted_index.leading | 71 |
| abstract_inverted_index.methods | 36, 173 |
| abstract_inverted_index.propose | 94 |
| abstract_inverted_index.reduced | 77 |
| abstract_inverted_index.search. | 32 |
| abstract_inverted_index.similar | 61 |
| abstract_inverted_index.utilize | 143 |
| abstract_inverted_index.without | 44 |
| abstract_inverted_index.However, | 33 |
| abstract_inverted_index.Ignoring | 48 |
| abstract_inverted_index.accuracy | 181 |
| abstract_inverted_index.accurate | 150 |
| abstract_inverted_index.capacity | 79 |
| abstract_inverted_index.distance | 65, 130 |
| abstract_inverted_index.encoding | 69, 74 |
| abstract_inverted_index.equipped | 115 |
| abstract_inverted_index.existing | 34, 171 |
| abstract_inverted_index.flexible | 31 |
| abstract_inverted_index.measures | 118 |
| abstract_inverted_index.networks | 162, 168 |
| abstract_inverted_index.preserve | 87 |
| abstract_inverted_index.proposed | 16 |
| abstract_inverted_index.prospers | 6 |
| abstract_inverted_index.recently | 17 |
| abstract_inverted_index.results. | 85 |
| abstract_inverted_index.scalable | 29 |
| abstract_inverted_index.topology | 123 |
| abstract_inverted_index.transfer | 190 |
| abstract_inverted_index.twostage | 200 |
| abstract_inverted_index.0.4%-3.6% | 179 |
| abstract_inverted_index.Embedding | 104 |
| abstract_inverted_index.automates | 4 |
| abstract_inverted_index.auxiliary | 134 |
| abstract_inverted_index.embedding | 113 |
| abstract_inverted_index.encoding, | 92, 140 |
| abstract_inverted_index.networks. | 11 |
| abstract_inverted_index.one-stage | 198 |
| abstract_inverted_index.precisely | 126 |
| abstract_inverted_index.surpasses | 204 |
| abstract_inverted_index.additional | 144 |
| abstract_inverted_index.continuous | 68 |
| abstract_inverted_index.detection. | 195 |
| abstract_inverted_index.detectors, | 201 |
| abstract_inverted_index.discovered | 163 |
| abstract_inverted_index.estimating | 127 |
| abstract_inverted_index.inaccurate | 73 |
| abstract_inverted_index.node-based | 52 |
| abstract_inverted_index.similarity | 50, 117 |
| abstract_inverted_index.structural | 145 |
| abstract_inverted_index.considering | 45 |
| abstract_inverted_index.correlation | 89 |
| abstract_inverted_index.efficiency. | 157 |
| abstract_inverted_index.information | 90, 146 |
| abstract_inverted_index.outperforms | 167 |
| abstract_inverted_index.performance | 26 |
| abstract_inverted_index.similarity. | 47 |
| abstract_inverted_index.sub-optimal | 83 |
| abstract_inverted_index.Architecture | 1, 100 |
| abstract_inverted_index.architecture | 39 |
| abstract_inverted_index.consistently | 166 |
| abstract_inverted_index.information. | 124 |
| abstract_inverted_index.relationship | 21 |
| abstract_inverted_index.architectures | 23 |
| abstract_inverted_index.counterparts. | 209 |
| abstract_inverted_index.inconsistency | 59 |
| abstract_inverted_index.classification | 175 |
| abstract_inverted_index.representation | 75, 78, 152 |
| abstract_inverted_index.Estimator-based | 12 |
| abstract_inverted_index.estimator-based | 35 |
| abstract_inverted_index.manually-crafted | 206 |
| abstract_inverted_index.Weisfeiler-Lehman | 135 |
| abstract_inverted_index.Multiply-Accumulates. | 187 |
| abstract_inverted_index.automatically-searched | 208 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |