Meiosis Networks

By Stephen Jose Hanson. 1989

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Citations in the corpus

Weight Agnostic Neural Networks (2019, background): “tolerant. Bayesian Neural Networks The weight parameters of a BNN [4, 5, 27, 47, 74, 84] are not ﬁxed values, but sampled from a distribution. While the parameters of this distribution can be learned [30, 41, 42, 59], the number of parameters is often greater than the number of weights. Recently, Ne”
Generative learning structures and processes for generalized connectionist networks (1993, unknown): “, generative learning (Honavar & Uhr, 1988; 1989a; 1989b) and related approaches (Ash, 1989; Diederich, 1988; Fahlman & Lebiere, 1990; Hanson, 1990; Gallant, 1990; Nadal, 1989; Rujan & Marchand, 1989).”
Many-Layered Learning (2002, unknown): “Several constructive methods add hidden units during learning, increasing the width of one or more existing layers (Ash, 1989; Hanson, 1990; Frean, 1990; Wynne-Jones, 1991; Utgoff & Precup, 1998).”
An evolutionary algorithm that constructs recurrent neural networks (1994, background): “Constructive algorithms initially assume a simple network and add nodes and links as warranted [2-8], while destructive methods start with a large network and prune off superfluous components [9-12].”
Analysis and Optimization of Convolutional Neural Network Architectures (2017, background): “desintheupper rightcorner. Theblacksquaresrepresentfrozenweightswhicharefoundbycorrelation maximizationwhereasthewhitesquaresaretrainableweights. 3.1.2. Meiosis Networks MeiosisNetworksareintroducedin[Han89]. IncontrasttomostMLPsandCNNs,where weightsaredeterministicandﬁxedatpredictiontime,eachweight”