Backpropagation through time

"BPTT" redirects here. For the running events originally known as the Bushy Park Time Trial, see parkrun.

Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks, such as Elman networks. The algorithm was independently derived by numerous researchers.[1][2][3]

  1. ^ Mozer, M. C. (1995). "A Focused Backpropagation Algorithm for Temporal Pattern Recognition". In Chauvin, Y.; Rumelhart, D. (eds.). Backpropagation: Theory, architectures, and applications. Hillsdale, NJ: Lawrence Erlbaum Associates. pp. 137–169. Retrieved 2017-08-21. {{cite book}}: |website= ignored (help)
  2. ^ Robinson, A. J. & Fallside, F. (1987). The utility driven dynamic error propagation network (Technical report). Cambridge University, Engineering Department. CUED/F-INFENG/TR.1.
  3. ^ Werbos, Paul J. (1988). "Generalization of backpropagation with application to a recurrent gas market model". Neural Networks. 1 (4): 339–356. doi:10.1016/0893-6080(88)90007-x.