The file describing a network is processed by the Aspirin parser and files containing C functions to implement that network are generated. This code can then be linked with an application which uses these routines to control the network. Detecting a sine wave in noise. The classic iris database. Learing to recognize 4 characters independent of rotation.
Autoregressive network learns a decaying sinusoid impulse response.
Autoregressive network learns to recognize a short sequence of orthonormal vectors. The backprop part of the monk paper. The MONK's problem were the basis of a first international comparison of learning algorithms. The backpropagation code generator produces simulations that run very efficiently. Aspirin simulations do best on vector machines when the networks are large, as exemplified by the Cray's performance.