Neuro-Symbolic Entropy Regularization Article Swipe
Kareem Ahmed
,
Eric Wang
,
Kai-Wei Chang
,
Guy Van den Broeck
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.11250
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.11250
In structured prediction, the goal is to jointly predict many output variables that together encode a structured object -- a path in a graph, an entity-relation triple, or an ordering of objects. Such a large output space makes learning hard and requires vast amounts of labeled data. Different approaches leverage alternate sources of supervision. One approach -- entropy regularization -- posits that decision boundaries should lie in low-probability regions. It extracts supervision from unlabeled examples, but remains agnostic to the structure of the output space. Conversely, neuro-symbolic approaches exploit the knowledge that not every prediction corresponds to a valid structure in the output space. Yet, they does not further restrict the learned output distribution. This paper introduces a framework that unifies both approaches. We propose a loss, neuro-symbolic entropy regularization, that encourages the model to confidently predict a valid object. It is obtained by restricting entropy regularization to the distribution over only valid structures. This loss is efficiently computed when the output constraint is expressed as a tractable logic circuit. Moreover, it seamlessly integrates with other neuro-symbolic losses that eliminate invalid predictions. We demonstrate the efficacy of our approach on a series of semi-supervised and fully-supervised structured-prediction experiments, where we find that it leads to models whose predictions are more accurate and more likely to be valid.
Related Topics
Concepts
Leverage (statistics)
Computer science
Exploit
Entropy (arrow of time)
Regularization (linguistics)
ENCODE
Artificial intelligence
Theoretical computer science
Graph
Cross entropy
Machine learning
Algorithm
Principle of maximum entropy
Biochemistry
Quantum mechanics
Computer security
Chemistry
Physics
Gene
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2201.11250
- https://arxiv.org/pdf/2201.11250
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226151932
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4226151932Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2201.11250Digital Object Identifier
- Title
-
Neuro-Symbolic Entropy RegularizationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-25Full publication date if available
- Authors
-
Kareem Ahmed, Eric Wang, Kai-Wei Chang, Guy Van den BroeckList of authors in order
- Landing page
-
https://arxiv.org/abs/2201.11250Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2201.11250Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2201.11250Direct OA link when available
- Concepts
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Leverage (statistics), Computer science, Exploit, Entropy (arrow of time), Regularization (linguistics), ENCODE, Artificial intelligence, Theoretical computer science, Graph, Cross entropy, Machine learning, Algorithm, Principle of maximum entropy, Biochemistry, Quantum mechanics, Computer security, Chemistry, Physics, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.propose | 124 |
| abstract_inverted_index.remains | 76 |
| abstract_inverted_index.sources | 51 |
| abstract_inverted_index.triple, | 26 |
| abstract_inverted_index.unifies | 120 |
| abstract_inverted_index.accurate | 210 |
| abstract_inverted_index.agnostic | 77 |
| abstract_inverted_index.approach | 55, 188 |
| abstract_inverted_index.circuit. | 169 |
| abstract_inverted_index.computed | 158 |
| abstract_inverted_index.decision | 62 |
| abstract_inverted_index.efficacy | 185 |
| abstract_inverted_index.extracts | 70 |
| abstract_inverted_index.learning | 38 |
| abstract_inverted_index.leverage | 49 |
| abstract_inverted_index.objects. | 31 |
| abstract_inverted_index.obtained | 142 |
| abstract_inverted_index.ordering | 29 |
| abstract_inverted_index.regions. | 68 |
| abstract_inverted_index.requires | 41 |
| abstract_inverted_index.restrict | 109 |
| abstract_inverted_index.together | 13 |
| abstract_inverted_index.Different | 47 |
| abstract_inverted_index.Moreover, | 170 |
| abstract_inverted_index.alternate | 50 |
| abstract_inverted_index.eliminate | 179 |
| abstract_inverted_index.examples, | 74 |
| abstract_inverted_index.expressed | 164 |
| abstract_inverted_index.framework | 118 |
| abstract_inverted_index.knowledge | 90 |
| abstract_inverted_index.structure | 80, 99 |
| abstract_inverted_index.tractable | 167 |
| abstract_inverted_index.unlabeled | 73 |
| abstract_inverted_index.variables | 11 |
| abstract_inverted_index.approaches | 48, 87 |
| abstract_inverted_index.boundaries | 63 |
| abstract_inverted_index.constraint | 162 |
| abstract_inverted_index.encourages | 131 |
| abstract_inverted_index.integrates | 173 |
| abstract_inverted_index.introduces | 116 |
| abstract_inverted_index.prediction | 94 |
| abstract_inverted_index.seamlessly | 172 |
| abstract_inverted_index.structured | 1, 16 |
| abstract_inverted_index.Conversely, | 85 |
| abstract_inverted_index.approaches. | 122 |
| abstract_inverted_index.confidently | 135 |
| abstract_inverted_index.corresponds | 95 |
| abstract_inverted_index.demonstrate | 183 |
| abstract_inverted_index.efficiently | 157 |
| abstract_inverted_index.prediction, | 2 |
| abstract_inverted_index.predictions | 207 |
| abstract_inverted_index.restricting | 144 |
| abstract_inverted_index.structures. | 153 |
| abstract_inverted_index.supervision | 71 |
| abstract_inverted_index.distribution | 149 |
| abstract_inverted_index.experiments, | 197 |
| abstract_inverted_index.predictions. | 181 |
| abstract_inverted_index.supervision. | 53 |
| abstract_inverted_index.distribution. | 113 |
| abstract_inverted_index.neuro-symbolic | 86, 127, 176 |
| abstract_inverted_index.regularization | 58, 146 |
| abstract_inverted_index.entity-relation | 25 |
| abstract_inverted_index.low-probability | 67 |
| abstract_inverted_index.regularization, | 129 |
| abstract_inverted_index.semi-supervised | 193 |
| abstract_inverted_index.fully-supervised | 195 |
| abstract_inverted_index.structured-prediction | 196 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |