Knowledge-infused Deep Learning
| Autor: | Manas Gaur, Ugur Kursuncu, Shweta Yadav, Ruwan Wickramarachchi, Amit P. Sheth |
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| Rok vydání: | 2020 |
| Předmět: |
020205 medical informatics
Artificial neural network Traceability Computer science business.industry Deep learning 020206 networking & telecommunications 02 engineering and technology Data science Variety (cybernetics) Subject-matter expert Interactivity 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Use case Artificial intelligence business |
| Zdroj: | HT |
| Popis: | Deep Learning has shown remarkable success during the last decade for essential tasks in computer vision and natural language processing. Yet, challenges remain in the development and deployment of artificial intelligence (AI) models in real-world cases, such as dependence on extensive data and trust, explainability, traceability, and interactivity. These challenges are amplified in high-risk fields, including healthcare, cyber threats, crisis response, autonomous driving, and future manufacturing. On the other hand, symbolic computing with knowledge graphs has shown significant growth in specific tasks with reliable performance. This tutorial (a) discusses the novel paradigm of knowledge-infused deep learning to synthesize neural computing with symbolic computing (b) describes different forms of knowledge and infusion methods in deep learning, and (c) discusses application-specific evaluation methods to assure explainability and reasoning using benchmark datasets and knowledge-resources. The resulting paradigm of "knowledge-infused learning'' combines knowledge from both domain expertise and physical models. A wide variety of techniques involving shallow, semi-deep, and deep infusion will be discussed along with the corresponding intuitions, limitations, use cases, and applications. More details can be found \urlhttp://kidl2020.aiisc.ai/. |
| Databáze: | OpenAIRE |
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