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Pruning backpropagation neural networks using modern stochastic optimisation techniques

https://doi.org/10.1007/bf01501173
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Learning in evolutive neural architectures: An ill-posed problem?
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Topology design of feedforward neural networks by genetic algorithms
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Novelty Detection and Neural Network Validation
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Neural networks for control systems—A survey
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Genetic algorithms and neural networks: optimizing connections and connectivity
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Application of neural networks to load-frequency control in power systems
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Artificial neural networks in process engineering
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Novelty detection and neural network validation
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Properties of neural networks with applications to modelling non-linear dynamical systems
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Neural network for system identification
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Modeling chemical process systems via neural computation
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Use of neural networks for sensor failure detection in a control system
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Pruning algorithms-a survey
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Identification and control of dynamical systems using neural networks
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Multivariable adaptive control of unknown nonlinear dynamic systems using neural networks
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Optimal Brain Surgeon and general network pruning
inferredcites →https://doi.org/10.1184/r1/6610403openalex-snapshot
The Cascade-Correlation learning architecture
inferredcites →https://doi.org/10.5860/choice.27-0936openalex-snapshot
Genetic algorithms in search, optimization, and machine learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Artificial Neural Networks Optimization by means of Evolutionary Algorithms
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A Multi-objective Particle Swarm Optimization for Neural Networks Pruning
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Centripetal SGD for Pruning Very Deep Convolutional Networks With Complicated Structure
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Compression of Neural Networks for Specialized Tasks via Value Locality
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Differential Evolution Based Layer-Wise Weight Pruning for Compressing Deep Neural Networks
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Modeling Driver Behavior in a Straight-line Emergency Situation
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Compact Deep Convolutional Neural Networks With Coarse Pruning
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Manipulating Identical Filter Redundancy for Efficient Pruning on Deep and Complicated CNN
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Convolutional neural networks pruning and its application to embedded vision systems
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