A Constructive Algorithm that Converges for Real-Valued Input Patterns

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Deep learning in neural networks: An overview (2014, neutral): “…towards simplicity) are implicit in constructive and pruning algorithms, e.g., layer-by-layer sequential network construction (e.g., Ash, 1989; Burgess, 1994; Fahlman, 1991; Fritzke, 1994; Gallant, 1988; Honavar & Uhr, 1988, 1993; Ivakhnenko, 1968, 1971; Moody, 1989; Parekh, Yang, & Honavar,…”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “As the history grows, to find an M with small bitsM + bitsH , step 4 uses sequential network construction: it regularly changes M ’s size by adding or pruning units and connections [111, 112, 8, 168, 59, 107, 304, 176, 141, 92, 143, 204, 53, 296, 106, 31, 57, 185, 283].”
A new evolutionary system for evolving artificial neural networks (1997, background): “Since CCA, PCA, and TA are all fully connected, the number of connections in EPNet-evolved ANN's is smaller as well.”
Constructive neural-network learning algorithms for pattern classification (2000, methodology): “0 and was dynamically updated at the end of each epoch to match the average net input of the neuron(s) during the entire epoch [6].”