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Salience-affected neural networks
Includes abstract.
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| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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Department of Electrical Engineering
2015
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| _version_ | 1867613154821799936 |
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| access_status_str | Open Access |
| author | Remmelzwaal, Leendert Amani |
| author2 | Tapson, Jonathan |
| author_browse | Remmelzwaal, Leendert Amani Tapson, Jonathan |
| author_facet | Tapson, Jonathan Remmelzwaal, Leendert Amani |
| author_sort | Remmelzwaal, Leendert Amani |
| collection | Thesis |
| description | Includes abstract. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/12111 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:38.662Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/12111 Salience-affected neural networks Remmelzwaal, Leendert Amani Tapson, Jonathan Ellis, GFR Electrical Engineering Includes abstract. Includes bibliographical references (leaves 46-49). In this research, the salience of an entity refers to its state or quality of standing out, or receiving increased attention, relative to neighboring entities. By neighbouring entities we refer to both spatial (i.e. similar visual objects) and temporal (i.e. related concepts). In this research we model the effect of non-local connections using an ANN, creating a salience-affected neural network (SANN). We adapt an ANN to embody the capacity to respond to an input salience signal and to produce a reverse salience signal during testing. The input salience signal applied during training to each node has the effect of varying the node’s thresholds, depending on the activation level of the node. Each node produces a nodal reverse salience signal during testing (a measure of the threshold bias for the individual node). The reverse salience signal is defined as the summation of the nodal reverse salience signals observed at each node. 2015-01-13T03:48:50Z 2015-01-13T03:48:50Z 2009 Master Thesis Masters MSc http://hdl.handle.net/11427/12111 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical Engineering Remmelzwaal, Leendert Amani Salience-affected neural networks |
| thesis_degree_str | Master's |
| title | Salience-affected neural networks |
| title_full | Salience-affected neural networks |
| title_fullStr | Salience-affected neural networks |
| title_full_unstemmed | Salience-affected neural networks |
| title_short | Salience-affected neural networks |
| title_sort | salience affected neural networks |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/12111 |
| work_keys_str_mv | AT remmelzwaalleendertamani salienceaffectedneuralnetworks |