Bayesian Compression for Deep Learning Article Swipe
Compression and computational efficiency in deep learning have become a problem of great significance. In this work, we argue that the most principled and effective way to attack this problem is by adopting a Bayesian point of view, where through sparsity inducing priors we prune large parts of the network. We introduce two novelties in this paper: 1) we use hierarchical priors to prune nodes instead of individual weights, and 2) we use the posterior uncertainties to determine the optimal fixed point precision to encode the weights. Both factors significantly contribute to achieving the state of the art in terms of compression rates, while still staying competitive with methods designed to optimize for speed or energy efficiency.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- http://resolver.tudelft.nl/uuid:c3157cba-e735-4992-a79f-d520318f945c
- OA Status
- green
- Cited By
- 8
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2963243833
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2963243833Canonical identifier for this work in OpenAlex
- Title
-
Bayesian Compression for Deep LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-01-01Full publication date if available
- Authors
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Christos Louizos, Karen Ullrich, Max WellingList of authors in order
- Landing page
-
https://resolver.tudelft.nl/uuid:c3157cba-e735-4992-a79f-d520318f945cPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://dare.uva.nl/personal/pure/en/publications/bayesian-compression-for-deep-learning(d11626c6-d457-4254-a3a9-b6c0623d157a).htmlDirect OA link when available
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Prior probability, ENCODE, Computer science, Compression (physics), Bayesian probability, Artificial intelligence, Bayesian network, Point (geometry), Machine learning, Algorithm, Mathematics, Geometry, Chemistry, Biochemistry, Materials science, Composite material, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
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2020: 1, 2019: 5, 2018: 2Per-year citation counts (last 5 years)
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.most | 21 |
| abstract_inverted_index.that | 19 |
| abstract_inverted_index.this | 15, 28, 55 |
| abstract_inverted_index.with | 107 |
| abstract_inverted_index.argue | 18 |
| abstract_inverted_index.fixed | 80 |
| abstract_inverted_index.great | 12 |
| abstract_inverted_index.large | 45 |
| abstract_inverted_index.nodes | 64 |
| abstract_inverted_index.parts | 46 |
| abstract_inverted_index.point | 35, 81 |
| abstract_inverted_index.prune | 44, 63 |
| abstract_inverted_index.speed | 113 |
| abstract_inverted_index.state | 94 |
| abstract_inverted_index.still | 104 |
| abstract_inverted_index.terms | 99 |
| abstract_inverted_index.view, | 37 |
| abstract_inverted_index.where | 38 |
| abstract_inverted_index.while | 103 |
| abstract_inverted_index.work, | 16 |
| abstract_inverted_index.attack | 27 |
| abstract_inverted_index.become | 8 |
| abstract_inverted_index.encode | 84 |
| abstract_inverted_index.energy | 115 |
| abstract_inverted_index.paper: | 56 |
| abstract_inverted_index.priors | 42, 61 |
| abstract_inverted_index.rates, | 102 |
| abstract_inverted_index.factors | 88 |
| abstract_inverted_index.instead | 65 |
| abstract_inverted_index.methods | 108 |
| abstract_inverted_index.optimal | 79 |
| abstract_inverted_index.problem | 10, 29 |
| abstract_inverted_index.staying | 105 |
| abstract_inverted_index.through | 39 |
| abstract_inverted_index.Bayesian | 34 |
| abstract_inverted_index.adopting | 32 |
| abstract_inverted_index.designed | 109 |
| abstract_inverted_index.inducing | 41 |
| abstract_inverted_index.learning | 6 |
| abstract_inverted_index.network. | 49 |
| abstract_inverted_index.optimize | 111 |
| abstract_inverted_index.sparsity | 40 |
| abstract_inverted_index.weights, | 68 |
| abstract_inverted_index.weights. | 86 |
| abstract_inverted_index.achieving | 92 |
| abstract_inverted_index.determine | 77 |
| abstract_inverted_index.effective | 24 |
| abstract_inverted_index.introduce | 51 |
| abstract_inverted_index.novelties | 53 |
| abstract_inverted_index.posterior | 74 |
| abstract_inverted_index.precision | 82 |
| abstract_inverted_index.contribute | 90 |
| abstract_inverted_index.efficiency | 3 |
| abstract_inverted_index.individual | 67 |
| abstract_inverted_index.principled | 22 |
| abstract_inverted_index.Compression | 0 |
| abstract_inverted_index.competitive | 106 |
| abstract_inverted_index.compression | 101 |
| abstract_inverted_index.efficiency. | 116 |
| abstract_inverted_index.hierarchical | 60 |
| abstract_inverted_index.computational | 2 |
| abstract_inverted_index.significance. | 13 |
| abstract_inverted_index.significantly | 89 |
| abstract_inverted_index.uncertainties | 75 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.9100000262260437 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.88097647 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |