Concept-Oriented Deep Learning: Generative Concept Representations
Abstract
Generative concept representations have three major advantages over discriminative ones: they can represent uncertainty, they support integration of learning and reasoning, and they are good for unsupervised and semi-supervised learning. We discuss probabilistic and generative deep learning, which generative concept representations are based on, and the use of variational autoencoders and generative adversarial networks for learning generative concept representations, particularly for concepts whose data are sequences, structured data or graphs.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2018
- DOI:
- 10.48550/arXiv.1811.06622
- arXiv:
- arXiv:1811.06622
- Bibcode:
- 2018arXiv181106622C
- Keywords:
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- Computer Science - Machine Learning;
- Computer Science - Artificial Intelligence;
- Statistics - Machine Learning