Bayesian Compression for Deep Learning Article Swipe
Christos Louizos
,
Karen Ullrich
,
Max Welling
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1705.08665
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1705.08665
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
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1705.08665
- https://arxiv.org/pdf/1705.08665
- OA Status
- green
- Cited By
- 349
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2619890685
All OpenAlex metadata
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https://openalex.org/W2619890685Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1705.08665Digital Object Identifier
- Title
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Bayesian Compression for Deep LearningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-05-24Full publication date if available
- Authors
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Christos Louizos, Karen Ullrich, Max WellingList of authors in order
- Landing page
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https://arxiv.org/abs/1705.08665Publisher landing page
- PDF URL
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https://arxiv.org/pdf/1705.08665Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1705.08665Direct OA link when available
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Prior probability, ENCODE, Computer science, Bayesian probability, Compression (physics), Artificial intelligence, Point (geometry), Bayesian network, Machine learning, Algorithm, Mathematics, Chemistry, Biochemistry, Geometry, Composite material, Gene, Materials scienceTop concepts (fields/topics) attached by OpenAlex
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349Total citation count in OpenAlex
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2025: 3, 2024: 11, 2023: 20, 2022: 18, 2021: 57Per-year citation counts (last 5 years)
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61Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| 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 | |
| countries_distinct_count | 0 |
| 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 |