Neural Network Exploration Using Optimal Experiment Design
Abstract
Consider the problem of learning input/output mappings through exploration, e.g. learning the kinematics or dynamics of a robotic manipulator. If actions are expensive and computation is cheap, then we should explore by selecting a trajectory through the in(cid:173) put space which gives us the most amount of information in the fewest number of steps. I discuss how results from the field of opti(cid:173) mal experiment design may be used to guide such exploration, and demonstrate its use on a simple kinematics problem.
Cite
Text
Cohn. "Neural Network Exploration Using Optimal Experiment Design." Neural Information Processing Systems, 1993.Markdown
[Cohn. "Neural Network Exploration Using Optimal Experiment Design." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/cohn1993neurips-neural/)BibTeX
@inproceedings{cohn1993neurips-neural,
title = {{Neural Network Exploration Using Optimal Experiment Design}},
author = {Cohn, David A.},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {679-686},
url = {https://mlanthology.org/neurips/1993/cohn1993neurips-neural/}
}