Backpropagation through structure

Backpropagation through structure (BPTS) is a gradient-based technique for training recursive neural nets (a superset of recurrent neural nets) and is extensively described in a 1996 paper written by Christoph Goller and Andreas Küchler.^[1]

References

^ Kuchler, Andreas (1996). "Learning Task-Dependent Distributed Representations by Backpropagation Through Structure". Proceedings of International Conference on Neural Networks (ICNN'96). Vol. 1. pp. 347–352. CiteSeerX 10.1.1.49.1968. doi:10.1109/ICNN.1996.548916. ISBN 0-7803-3210-5. S2CID 6536466.

This artificial intelligence-related article is a stub. You can help Wikipedia by expanding it.

[1] Kuchler, Andreas (1996). "Learning Task-Dependent Distributed Representations by Backpropagation Through Structure". Proceedings of International Conference on Neural Networks (ICNN'96). Vol. 1. pp. 347–352. CiteSeerX 10.1.1.49.1968. doi:10.1109/ICNN.1996.548916. ISBN 0-7803-3210-5. S2CID 6536466.

[1]