An Analysis of Single-Layer Networks in Unsupervised Feature Learning
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Summary
The results show that large numbers of hidden nodes and dense feature extraction are critical to achieving high performance—so critical, in fact, that when these parameters are pushed to their limits, they achieve state-of-the-art performance on both CIFAR-10 and NORB using only a single layer of features.
- Type
- article
- Published
- 2011-12-01
- Cited by
- 4,557
- References
- 36
- OpenAlex
- https://openalex.org/W2118858186
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:308212
Keywords
Computer science, Hyperparameter, Cluster analysis, Artificial intelligence, Feature (linguistics)
References
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- Compute Less to Get More: Using ORC to Improve Sparse Filtering
- Towards adaptive learning and inference : applications to hyperparameter tuning and astroparticle physics
- Learning to see like children: proof of concept
- Segmentation of brain MRI structures with deep machine learning
- Collaborative hyperparameter tuning
- On Random Weights and Unsupervised Feature Learning
- Learning by Stretching Deep Networks
- When Multivariate Forecasting Meets Unsupervised Feature Learning - Towards a Novel Anomaly Detection Framework for Decision Support
- Parallelized Deep Neural Networks for Distributed Intelligent Systems
- Learning Invariant Color Features for Person Reidentification
- Inference Machines: Parsing Scenes via Iterated Predictions
- Deep Unsupervised Feature Learning for Natural Language Processing
- Online Multi-Stage Deep Architectures for Feature Extraction and Object Recognition
- Discovery of Deep Structure from Unlabeled Data
- Anytime Prediction: Efficient Ensemble Methods for Any Computational Budget
- Development of a Response Planner using the UCT Algorithm for Cyber Defense
- Unsupervised Learning of Object Descriptors and Compositions
- When are Overcomplete Representations Identifiable? Uniqueness of Tensor Decompositions Under Expansion Constraints
- Learning Hierarchical Feature Extractors For Image Recognition
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