Context-dependent deep learning
Modeling and Using Context
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Abstract
Explicitly representing an agent’s context has been shown to have many benefits, which should also apply to machine learning. In this paper, we describe an approach to do this called context-dependent deep learning (CDDL), which is based on earlier work in context-mediated behavior (CMB) that uses contextual schemas (c-schemas) to represent clas-ses of situations along with knowledge useful in them. These are then recalled, and they guide reasoning in the corre-sponding contexts. CDDL stores knowledge about deep neural network structure and weights in c-schemas, which al-lows context-specific learning. Our work is being developed in the domain of seabird detection in aerial images of islands for use by biologists.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Context-dependent deep learning |
| Series title | Modeling and Using Context |
| DOI | 10.21494/ISTE.OP.2021.0690 |
| Volume | 4 |
| Year Published | 2021 |
| Language | English |
| Publisher | ISTE OpenScience |
| Contributing office(s) | Coop Res Unit Leetown |
| Description | 7 p. |
| Google Analytic Metrics | Metrics page |