Unsupervised learning and continual learning in neural networks

Unsupervised learning and continual learning in neural networks

<p>For decades research has pursued the ambitious goal of designing computer models that learn to solve problems as effectively as humans can. Artificial neural networks -- generic, optimizable models that were originally inspired by biological neurons in the brain -- appear to provide a promi...

Bibliográfalaš dieđut
Váldodahkki:	Ji, X
Eará dahkkit:	Vedaldi, A
Materiálatiipa:	Oahppočájánas
Giella:	English
Almmustuhtton:	2021
Fáttát:	Artificial intelligence Neural networks (Computer science) Computer vision

Geahča maid

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Artificial Neural Networks - ICANN 2007 [electronic resource] : 17th International Conference, Porto, Portugal, September 9-13, 2007: Proceedings, Part II /
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Almmustuhtton: (2007)

Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm
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Object classification using deep learning [electronic resource] /
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Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning
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Almmustuhtton: (2021-01-01)

Neural Network Preference Learning Approaches For Improving Agent-Based Meeting Scheduling Problems
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Review and analysis of deep neural network models for Alzheimer's disease classification using brain medical resonance imaging
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Understanding convolutional neural networks
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Celiac Disease Deep Learning Image Classification Using Convolutional Neural Networks
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Almmustuhtton: (2024-08-01)

Disrupted visual input unveils the computational details of artificial neural networks for face perception
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Subspace learning of neural networks /
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Federated learning for graph neural networks
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Almmustuhtton: (2023)

Adaptive memory networks with self-supervised learning for unsupervised anomaly detection
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Three-term back propagation neural network for moisture prediction /
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Matching sensor ontologies with unsupervised neural network with competitive learning
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