Understanding the wiring evolution in differentiable neural architecture search Article Swipe
Sirui Xie
,
Shoukang Hu
,
Xinjiang Wang
,
Chunxiao Liu
,
Jianping Shi
,
Xunying Liu
,
Dahua Lin
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2009.01272
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2009.01272
Controversy exists on whether differentiable neural architecture search methods discover wiring topology effectively. To understand how wiring topology evolves, we study the underlying mechanism of several existing differentiable NAS frameworks. Our investigation is motivated by three observed searching patterns of differentiable NAS: 1) they search by growing instead of pruning; 2) wider networks are more preferred than deeper ones; 3) no edges are selected in bi-level optimization. To anatomize these phenomena, we propose a unified view on searching algorithms of existing frameworks, transferring the global optimization to local cost minimization. Based on this reformulation, we conduct empirical and theoretical analyses, revealing implicit inductive biases in the cost's assignment mechanism and evolution dynamics that cause the observed phenomena. These biases indicate strong discrimination towards certain topologies. To this end, we pose questions that future differentiable methods for neural wiring discovery need to confront, hoping to evoke a discussion and rethinking on how much bias has been enforced implicitly in existing NAS methods.
Related Topics
Concepts
Differentiable function
Pruning
Computer science
Network topology
Deep neural networks
Mechanism (biology)
Artificial neural network
Topology (electrical circuits)
Artificial intelligence
Theoretical computer science
Mathematics
Biology
Operating system
Combinatorics
Epistemology
Philosophy
Mathematical analysis
Agronomy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2009.01272
- OA Status
- green
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3082801207
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https://doi.org/10.48550/arxiv.2009.01272Digital Object Identifier
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Understanding the wiring evolution in differentiable neural architecture searchWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
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2020-09-02Full publication date if available
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Sirui Xie, Shoukang Hu, Xinjiang Wang, Chunxiao Liu, Jianping Shi, Xunying Liu, Dahua LinList of authors in order
- Landing page
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https://arxiv.org/abs/2009.01272Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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- Concepts
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Differentiable function, Pruning, Computer science, Network topology, Deep neural networks, Mechanism (biology), Artificial neural network, Topology (electrical circuits), Artificial intelligence, Theoretical computer science, Mathematics, Biology, Operating system, Combinatorics, Epistemology, Philosophy, Mathematical analysis, AgronomyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.certain | 123 |
| abstract_inverted_index.conduct | 95 |
| abstract_inverted_index.growing | 46 |
| abstract_inverted_index.instead | 47 |
| abstract_inverted_index.methods | 8, 134 |
| abstract_inverted_index.propose | 72 |
| abstract_inverted_index.several | 25 |
| abstract_inverted_index.towards | 122 |
| abstract_inverted_index.unified | 74 |
| abstract_inverted_index.whether | 3 |
| abstract_inverted_index.bi-level | 65 |
| abstract_inverted_index.discover | 9 |
| abstract_inverted_index.dynamics | 111 |
| abstract_inverted_index.enforced | 155 |
| abstract_inverted_index.evolves, | 18 |
| abstract_inverted_index.existing | 26, 80, 158 |
| abstract_inverted_index.implicit | 101 |
| abstract_inverted_index.indicate | 119 |
| abstract_inverted_index.methods. | 160 |
| abstract_inverted_index.networks | 52 |
| abstract_inverted_index.observed | 36, 115 |
| abstract_inverted_index.patterns | 38 |
| abstract_inverted_index.pruning; | 49 |
| abstract_inverted_index.selected | 63 |
| abstract_inverted_index.topology | 11, 17 |
| abstract_inverted_index.analyses, | 99 |
| abstract_inverted_index.anatomize | 68 |
| abstract_inverted_index.confront, | 141 |
| abstract_inverted_index.discovery | 138 |
| abstract_inverted_index.empirical | 96 |
| abstract_inverted_index.evolution | 110 |
| abstract_inverted_index.inductive | 102 |
| abstract_inverted_index.mechanism | 23, 108 |
| abstract_inverted_index.motivated | 33 |
| abstract_inverted_index.preferred | 55 |
| abstract_inverted_index.questions | 130 |
| abstract_inverted_index.revealing | 100 |
| abstract_inverted_index.searching | 37, 77 |
| abstract_inverted_index.algorithms | 78 |
| abstract_inverted_index.assignment | 107 |
| abstract_inverted_index.discussion | 146 |
| abstract_inverted_index.implicitly | 156 |
| abstract_inverted_index.phenomena, | 70 |
| abstract_inverted_index.phenomena. | 116 |
| abstract_inverted_index.rethinking | 148 |
| abstract_inverted_index.underlying | 22 |
| abstract_inverted_index.understand | 14 |
| abstract_inverted_index.Controversy | 0 |
| abstract_inverted_index.frameworks, | 81 |
| abstract_inverted_index.frameworks. | 29 |
| abstract_inverted_index.theoretical | 98 |
| abstract_inverted_index.topologies. | 124 |
| abstract_inverted_index.architecture | 6 |
| abstract_inverted_index.effectively. | 12 |
| abstract_inverted_index.optimization | 85 |
| abstract_inverted_index.transferring | 82 |
| abstract_inverted_index.investigation | 31 |
| abstract_inverted_index.minimization. | 89 |
| abstract_inverted_index.optimization. | 66 |
| abstract_inverted_index.differentiable | 4, 27, 40, 133 |
| abstract_inverted_index.discrimination | 121 |
| abstract_inverted_index.reformulation, | 93 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |