Function space analysis of deep learning representation layers
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Summary
A function space approach to Representation Learning and the analysis of the representation layers in deep learning architectures is proposed and a weak-type Besov smoothness index is computed that quantifies the geometry of the clustering in the feature space.
- Type
- preprint
- Published
- 2017-10-09
- Cited by
- 4
- References
- 44
- Access
- Open access
- OpenAlex
- https://openalex.org/W2763774376
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:22789450
Keywords
Smoothness, Gradient boosting, Representation (politics), Computer science, Artificial intelligence
References
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- Nonlinear piecewise polynomial approximation beyond Besov spaces
- The Elements of Statistical Learning
- Two-Level-Split Decomposition of Anisotropic Besov Spaces
- An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation
- Pruning of Random Forest classifiers: A survey and future directions
- A wavelet tour of signal processing
- Theoretical Comparison between the Gini Index and Information Gain Criteria
- L1-based compression of random forest models
- Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval
- Predictive Ensemble Pruning by Expectation Propagation
- Representation Learning: A Review and New Perspectives
- Image compression through wavelet transform coding
- Understanding variable importances in forests of randomized trees
- A random forest guided tour
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