Abstract
We extend the analysis of parameter sensitivity and interdependence to two digital artificial neural network structures, the backpropagation and ALCOVE. This paper compares the two networks, and we generalize to show that a highly sensitive weight contributes more to the prediction of the network than does an insensitive parameter. This suggests that the information structure of an input pattern can be determined by looking at the sensitivity of the interconnection weights, which has ramifications in network design. Additionally, results from a different set of simulations indicate that information about weight sensitivity and interdependence is predictive of the learning behavior of the networks.
Original language | English |
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Title of host publication | Proceedings - IEEE International Symposium on Circuits and Systems |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 445-448 |
Number of pages | 4 |
Volume | 3 |
Publication status | Published - 1996 |
Event | Proceedings of the 1996 IEEE International Symposium on Circuits and Systems, ISCAS. Part 1 (of 4) - Atlanta, GA, USA Duration: 12 May 1996 → 15 May 1996 |
Conference
Conference | Proceedings of the 1996 IEEE International Symposium on Circuits and Systems, ISCAS. Part 1 (of 4) |
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City | Atlanta, GA, USA |
Period | 12/05/96 → 15/05/96 |
Research Groups and Themes
- Memory