On Causal Inference for Data-free Structured Pruning Article Swipe
Martin Ferianc
,
Anush Sankaran
,
Olivier Mastropietro
,
Ehsan Saboori
,
Quentin Cappart
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.10229
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.10229
Neural networks (NNs) are making a large impact both on research and industry. Nevertheless, as NNs' accuracy increases, it is followed by an expansion in their size, required number of compute operations and energy consumption. Increase in resource consumption results in NNs' reduced adoption rate and real-world deployment impracticality. Therefore, NNs need to be compressed to make them available to a wider audience and at the same time decrease their runtime costs. In this work, we approach this challenge from a causal inference perspective, and we propose a scoring mechanism to facilitate structured pruning of NNs. The approach is based on measuring mutual information under a maximum entropy perturbation, sequentially propagated through the NN. We demonstrate the method's performance on two datasets and various NNs' sizes, and we show that our approach achieves competitive performance under challenging conditions.
Related Topics
Concepts
Computer science
Inference
Pruning
Software deployment
Artificial neural network
Entropy (arrow of time)
Deep neural networks
Causal inference
Artificial intelligence
Machine learning
Energy consumption
Data mining
Econometrics
Mathematics
Agronomy
Biology
Ecology
Operating system
Physics
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2112.10229
- https://arxiv.org/pdf/2112.10229
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226213750
All OpenAlex metadata
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https://openalex.org/W4226213750Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2112.10229Digital Object Identifier
- Title
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On Causal Inference for Data-free Structured PruningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-12-19Full publication date if available
- Authors
-
Martin Ferianc, Anush Sankaran, Olivier Mastropietro, Ehsan Saboori, Quentin CappartList of authors in order
- Landing page
-
https://arxiv.org/abs/2112.10229Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2112.10229Direct link to full text PDF
- Open access
-
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/2112.10229Direct OA link when available
- Concepts
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Computer science, Inference, Pruning, Software deployment, Artificial neural network, Entropy (arrow of time), Deep neural networks, Causal inference, Artificial intelligence, Machine learning, Energy consumption, Data mining, Econometrics, Mathematics, Agronomy, Biology, Ecology, Operating system, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2022: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.accuracy | 16 |
| abstract_inverted_index.achieves | 132 |
| abstract_inverted_index.adoption | 43 |
| abstract_inverted_index.approach | 76, 97, 131 |
| abstract_inverted_index.audience | 62 |
| abstract_inverted_index.datasets | 121 |
| abstract_inverted_index.decrease | 68 |
| abstract_inverted_index.followed | 20 |
| abstract_inverted_index.method's | 117 |
| abstract_inverted_index.networks | 1 |
| abstract_inverted_index.required | 27 |
| abstract_inverted_index.research | 10 |
| abstract_inverted_index.resource | 37 |
| abstract_inverted_index.available | 58 |
| abstract_inverted_index.challenge | 78 |
| abstract_inverted_index.expansion | 23 |
| abstract_inverted_index.industry. | 12 |
| abstract_inverted_index.inference | 82 |
| abstract_inverted_index.measuring | 101 |
| abstract_inverted_index.mechanism | 89 |
| abstract_inverted_index.Therefore, | 49 |
| abstract_inverted_index.compressed | 54 |
| abstract_inverted_index.deployment | 47 |
| abstract_inverted_index.facilitate | 91 |
| abstract_inverted_index.increases, | 17 |
| abstract_inverted_index.operations | 31 |
| abstract_inverted_index.propagated | 110 |
| abstract_inverted_index.real-world | 46 |
| abstract_inverted_index.structured | 92 |
| abstract_inverted_index.challenging | 136 |
| abstract_inverted_index.competitive | 133 |
| abstract_inverted_index.conditions. | 137 |
| abstract_inverted_index.consumption | 38 |
| abstract_inverted_index.demonstrate | 115 |
| abstract_inverted_index.information | 103 |
| abstract_inverted_index.performance | 118, 134 |
| abstract_inverted_index.consumption. | 34 |
| abstract_inverted_index.perspective, | 83 |
| abstract_inverted_index.sequentially | 109 |
| abstract_inverted_index.Nevertheless, | 13 |
| abstract_inverted_index.perturbation, | 108 |
| abstract_inverted_index.impracticality. | 48 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile |