Fast Pruning Using Principal Components

By A. U. Levin, Todd K. Leen, John E. Moody. 1993

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “, 1994), unit pruning (White, 1989; Mozer and Smolensky, 1989; Levin et al., 1994), weight pruning, e.”
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].”
Automatic early stopping using cross validation: quantifying the criteria (1998, background): “…to reduce the number of parameters, i.e., the number of dimensions of the parameter space, are greedy constructive learning (e.g. Fahlman & Lebiere, 1990), pruning (e.g. Le Cun, Denker & Solla, 1990; Hassibi & Stork, 1992; Levin, Leen & Moody, 1994), or weight sharing (e.g. Nowlan & Hinton, 1992).”
Flat Minima (1997, unknown): “…(Fahlman & Lebiere, 1990; Ash, 1989; Moody, 1989), input pruning (Moody, 1992; Refenes et al., 1994), unit pruning (White, 1989; Mozer & Smolensky, 1989; Levin et al., 1994), weight pruning, e.g. \optimal brain damage" (LeCun et al., 1990), \optimal brain surgeon" (Hassibi & Stork, 1993).”
Early Stopping-But When? (1996, background): “Techniques for reducing the number of parameters are greedy constructive learning [7], pruning [5, 12, 14], or weight sharing [18].”