Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?
The present phase of Machine Learning is characterized by supervised learning algorithms relying on large sets of labeled examples (n → ∞). The next phase is likely to focus on algorithms capable of learning from very few labeled examples (n → ∞), like humans seem able to do. We propose an approach...
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| Format: | Working Paper |
| Language: | en_US |
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Center for Brains, Minds and Machines (CBMM), arXiv
2014
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| Online Access: | http://hdl.handle.net/1721.1/90566 |