Reverse TDNN: An Architecture for Trajectory Generation
Abstract
The backpropagation algorithm can be used for both recognition and gen(cid:173) eration of time trajectories. When used as a recognizer, it has been shown that the performance of a network can be greatly improved by adding structure to the architecture. The same is true in trajectory generation. In particular a new architecture corresponding to a "reversed" TDNN is proposed. Results show dramatic improvement of performance in the gen(cid:173) eration of hand-written characters. A combination of TDNN and reversed TDNN for compact encoding is also suggested.
Cite
Text
Simard and Le Cun. "Reverse TDNN: An Architecture for Trajectory Generation." Neural Information Processing Systems, 1991.Markdown
[Simard and Le Cun. "Reverse TDNN: An Architecture for Trajectory Generation." Neural Information Processing Systems, 1991.](https://mlanthology.org/neurips/1991/simard1991neurips-reverse/)BibTeX
@inproceedings{simard1991neurips-reverse,
title = {{Reverse TDNN: An Architecture for Trajectory Generation}},
author = {Simard, Patrice and Le Cun, Yann},
booktitle = {Neural Information Processing Systems},
year = {1991},
pages = {579-588},
url = {https://mlanthology.org/neurips/1991/simard1991neurips-reverse/}
}