Representation learning¶
Bengio, Y., Courville, & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Representation
- Computer science and software engineering uses representation for data structures, schemas, encodings (JSON, XML, Protocol Buffers), serialisation formats, knowledge-representation languages (OWL, RDF), and the learned representations inside neural networks where the representation-learning programme
This source(Standard review article articulating the representation-learning programme in deep learning: the goal of constructing learned mappings from raw input to feature representations that disentangle factors of variation and support downstream task performance. Cited here as the canonical pointer into the modern ML representation-learning literature.)
- Computer science and software engineering uses representation for data structures, schemas, encodings (JSON, XML, Protocol Buffers), serialisation formats, knowledge-representation languages (OWL, RDF), and the learned representations inside neural networks where the representation-learning programme
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Registry ID ref:6c7eb94d6e13 · see in the full table