RANDOM NEURAL NETWORK METHODS AND DEEP LEARNING

DOI10.1017/S026996481800058XzbMath1493.68335OpenAlexW2912797248MaRDI QIDQ5070876

Yonghua Yin

Publication date: 14 April 2022

Published in: Probability in the Engineering and Informational Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1017/s026996481800058x

zbMATH Keywords

stochastic modelingqueueing theorycomputational probability

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Queueing theory (aspects of probability theory) (60K25) Neural nets and related approaches to inference from stochastic processes (62M45)

Uses Software

• NEURON
• UCI-ml
• LIBSVM
• darch
• Pynn
• TensorFlow
• CIFAR
• Brian
• NEST
• ADADELTA
• SpiNNaker

Cites Work

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• Function approximation by random neural networks with a bounded number of layers
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• Reducing the Dimensionality of Data with Neural Networks
• RANDOM NEURAL NETWORK LEARNING HEURISTICS
• Stability of Product Form G-Networks
• Adaptive Inertia Weight Particle Swarm Optimization
• Random Neural Networks with Synchronized Interactions
• Queues with negative arrivals
• Product-form queueing networks with negative and positive customers
• G-networks by triggered customer movement
• Learning in the feed-forward Random Neural Network: A Critical Review
• Neural Network Learning
• Learning the parts of objects by non-negative matrix factorization
• Projective Nonnegative Graph Embedding
• A Fast Learning Algorithm for Deep Belief Nets
• The Monte Carlo Method
• A logical calculus of the ideas immanent in nervous activity

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