title: Size invariance does not hold for connectionist models: dangers of using a toy model.
abstract: Connectionist models with backpropagation learning rule are known to have a serious problem called catastrophic interference or forgetting, although there have been several reports showing that the interference can be relatively mild with orthogonal inputs. The present study investigated the extent of interference using orthogonal inputs with varying network sizes. One would naturally assume that results obtained from small networks could be extrapolated for larger networks. Unexpectedly, the use of small networks was shown to worsen performance. This result has important implications for interpreting some data in the literature and cautions against the use of a toy model.
authors.affiliation: Department of Educational Psychology, Waseda University, Tokyo 169-8050, Japan. yamag-psy@kurenai.waseda.jp
authors.forename: Makoto
authors.initials: M
authors.lastname: Yamaguchi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003199:Computer Systems; D008959:Models, Neurological; D016571:Neural Networks, Computer