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We design a self size-estimating feed-forward network (SSFN) using a jointoptimization approach for estimation of number of layers, number of nodes andlearning of weight matrices. The learning algorithm has a low computationalcomplexity, preferably within few minutes using a laptop. In addition thealgorithm has a limited need for human intervention to tune parameters. SSFNgrows from a small-size network to a large-size network, guaranteeing amonotonically non-increasing cost with addition of nodes and layers. Thelearning approach uses judicious a combination of `lossless flow property' ofsome activation functions, convex optimization and instance of random matrix.Consistent performance -- low variation across Monte-Carlo trials -- is foundfor inference performance (classification accuracy) and estimation of networksize.

SSFN -- Self Size-estimating Feed-forward Network with Low Complexity, Limited Need for Human Intervention, and Consistent Behaviour across Trials