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Pruning is the practice of removing parameters (which may entail removing individual parameters, or parameters in groups such as by neurons) from an existing artificial neural networks. [1] The goal of this process is to maintain accuracy of the network while increasing its efficiency .
NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth Stanley and Risto Miikkulainen in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting ...
Pre-pruning procedures prevent a complete induction of the training set by replacing a stop criterion in the induction algorithm (e.g. max. Tree depth or information gain (Attr)> minGain). Pre-pruning methods are considered to be more efficient because they do not induce an entire set, but rather trees remain small from the start.
Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, and rules. [1] It is most commonly applied in artificial life, general game playing [2] and evolutionary robotics.
Xiangyu Chang; Yingcong Li; Samet Oymak; Christos Thrampoulidis (2021). "Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence. 35 (8). arXiv: 2012.08749.
Model compression (e.g. quantization and pruning of model parameters) can be applied to a deep neural network after it has been trained. [19] In the SqueezeNet paper, the authors demonstrated that a model compression technique called Deep Compression can be applied to SqueezeNet to further reduce the size of the parameter file from 5 MB to 500 ...
In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [1] [2] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain. Artificial ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]