How to Explain Neural Networks: A perspective of data space division. Article Swipe
Hangcheng Dong
,
Bingguo Liu
,
Fengdong Chen
,
Dong Ye
,
Guodong Liu
·
YOU?
·
· 2021
· Open Access
·
YOU?
·
· 2021
· Open Access
·
Interpretability of intelligent algorithms represented by deep learning has been yet an open problem. We discuss the shortcomings of the existing explainable method based on the two attributes of explanation, which are called completeness and explicitness. Furthermore, we point out that a model that completely relies on feed-forward mapping is extremely easy to cause inexplicability because it is hard to quantify the relationship between this mapping and the final model. Based on the perspective of the data space division, the principle of complete local interpretable model-agnostic explanations (CLIMEP) is proposed in this paper. To study the classification problems, we further discussed the equivalence of the CLIMEP and the decision boundary. As a matter of fact, it is also difficult to implementation of CLIMEP. To tackle the challenge, motivated by the fact that a fully-connected neural network (FCNN) with piece-wise linear activation functions (PWLs) can partition the input space into several linear regions, we extend this result to arbitrary FCNNs by the strategy of linearizing the activation functions. Applying this technique to solving classification problems, it is the first time that the complete decision boundary of FCNNs has been able to be obtained. Finally, we propose the DecisionNet (DNet), which divides the input space by the hyper-planes of the decision boundary. Hence, each linear interval of the DNet merely contains samples of the same label. Experiments show that the surprising model compression efficiency of the DNet with an arbitrary controlled precision.
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- Type
- preprint
- Language
- en
- Landing Page
- http://export.arxiv.org/pdf/2105.07831
- OA Status
- green
- Cited By
- 1
- References
- 41
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3160834628
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- OpenAlex ID
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https://openalex.org/W3160834628Canonical identifier for this work in OpenAlex
- Title
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How to Explain Neural Networks: A perspective of data space division.Work title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-05-17Full publication date if available
- Authors
-
Hangcheng Dong, Bingguo Liu, Fengdong Chen, Dong Ye, Guodong LiuList of authors in order
- Landing page
-
https://export.arxiv.org/pdf/2105.07831Publisher 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://export.arxiv.org/pdf/2105.07831Direct OA link when available
- Concepts
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Interpretability, Computer science, Artificial intelligence, Artificial neural network, Boundary (topology), Perspective (graphical), Equivalence (formal languages), Linear space, Algorithm, Theoretical computer science, Mathematics, Discrete mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1Per-year citation counts (last 5 years)
- References (count)
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41Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.difficult | 118 |
| abstract_inverted_index.discussed | 100 |
| abstract_inverted_index.division, | 78 |
| abstract_inverted_index.extremely | 50 |
| abstract_inverted_index.functions | 141 |
| abstract_inverted_index.motivated | 127 |
| abstract_inverted_index.obtained. | 191 |
| abstract_inverted_index.partition | 144 |
| abstract_inverted_index.principle | 80 |
| abstract_inverted_index.problems, | 97, 173 |
| abstract_inverted_index.technique | 169 |
| abstract_inverted_index.activation | 140, 165 |
| abstract_inverted_index.algorithms | 3 |
| abstract_inverted_index.attributes | 27 |
| abstract_inverted_index.challenge, | 126 |
| abstract_inverted_index.completely | 44 |
| abstract_inverted_index.controlled | 238 |
| abstract_inverted_index.efficiency | 231 |
| abstract_inverted_index.functions. | 166 |
| abstract_inverted_index.piece-wise | 138 |
| abstract_inverted_index.precision. | 239 |
| abstract_inverted_index.surprising | 228 |
| abstract_inverted_index.DecisionNet | 196 |
| abstract_inverted_index.Experiments | 224 |
| abstract_inverted_index.compression | 230 |
| abstract_inverted_index.equivalence | 102 |
| abstract_inverted_index.explainable | 21 |
| abstract_inverted_index.intelligent | 2 |
| abstract_inverted_index.linearizing | 163 |
| abstract_inverted_index.perspective | 73 |
| abstract_inverted_index.represented | 4 |
| abstract_inverted_index.Furthermore, | 36 |
| abstract_inverted_index.completeness | 33 |
| abstract_inverted_index.explanation, | 29 |
| abstract_inverted_index.explanations | 86 |
| abstract_inverted_index.feed-forward | 47 |
| abstract_inverted_index.hyper-planes | 205 |
| abstract_inverted_index.relationship | 62 |
| abstract_inverted_index.shortcomings | 17 |
| abstract_inverted_index.explicitness. | 35 |
| abstract_inverted_index.interpretable | 84 |
| abstract_inverted_index.classification | 96, 172 |
| abstract_inverted_index.implementation | 120 |
| abstract_inverted_index.model-agnostic | 85 |
| abstract_inverted_index.fully-connected | 133 |
| abstract_inverted_index.inexplicability | 54 |
| abstract_inverted_index.Interpretability | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |