Essays about: "Meta-Learning"
Showing result 1 - 5 of 7 essays containing the word Meta-Learning.
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1. EXPLORING TEST CASE DESIGN APPROACHES FOR META-LEARNING MODELS
University essay from Mälardalens universitet/Akademin för innovation, design och teknikAbstract : Meta-learning, which allows individuals to learn from a collection of algorithms, is currently one of the most essential and cutting-edge deep-learning issues. Because of their widespread applicability, these algorithms are inextricably linked to essential systems and human lives, and the necessity to test and debug such crucial systems is apparent. READ MORE
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2. A Systematic Literature Review on Meta Learning for Predictive Maintenance in Industry 4.0
University essay from Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)Abstract : Recent refinements in Industry 4.0 and Machine Learning demonstrate the positive effects of using deep learning models for intelligent maintenance. The primary benefit of Deep Learning (DL) is its capability to extract attributes and make fast, accurate, and automated predictions without supervision. READ MORE
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3. Insights into Model-Agnostic Meta-Learning on Reinforcement Learning Tasks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Meta-learning has been gaining traction in the Deep Learning field as an approach to build models that are able to efficiently adapt to new tasks after deployment. Contrary to conventional Machine Learning approaches, which are trained on a specific task (e. READ MORE
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4. Model-Agnostic Meta-Learning for Digital Pathology
University essay from Linköpings universitet/DatorseendeAbstract : The performance of conventional deep neural networks tends to degrade when a domain shift is introduced, such as collecting data from a new site. Model-Agnostic Meta-Learning, or MAML, has achieved state-of-the-art performance in few-shot learning by finding initial parameters that adapt easily for new tasks. READ MORE
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5. Few-Shot Learning with Deep Neural Networks for Visual Quality Control: Evaluations on a Production Line
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Having a well representative and adequate amount of data samples plays an important role in the success of deep learning algorithms used for image recognition. On the other hand, collecting and manually labeling a large-scale dataset requires a great deal of human interaction which in turn is very timeconsuming. READ MORE
