Supervised self-coding in multilayered feedforward networks

Supervised neural-network learning algorithms have proven very successful at solving a variety of learning problems. However, they suffer from a common problem of requiring explicit output labels. This requirement makes such algorithms implausible as biological models. In this paper, it is shown that pattern classification can be achieved, in a multilayered feedforward neural network, without requiring explicit output labels, by a process of supervised self-coding. The class projection is achieved by optimizing appropriate within-class uniformity, and between-class discernability criteria. The mapping function and the class labels are developed together, iteratively using the derived self-coding backpropagation algorithm. The ability of the self-coding network to generalize on unseen data is also experimentally evaluated on real data sets, and compares favorably with the traditional labeled supervision with neural networks. However, interesting features emerge out of the proposed self-coding supervision, which are absent in conventional approaches. The further implications of supervised self-coding with neural networks are also discussed.     

Performance measurement,visualization and modeling of parallel and distributed programs using the AIMS toolkit

Writing large-scale parallel and distributed scientific applications that make optimum use of the multiprocessor is a challenging problem. Typically, computational resources are underused due to performance failures in the application being executed. Performance-tuning tools are essential for exposing these performance failures and for suggesting ways to improve program performance. In this paper, we first address fundamental issues in building useful performance-tuning tools and then describe our experience with the AIMS toolkit for tuning parallel and distributed programs on a variety of platforms. AIMS supports source-code instrumentation, run-time monitoring, graphical execution profiles, performance indices and automated modeling techniques as ways to expose performance problems of programs. Using several examples representing a broad range of scientific applications, we illustrate AIMS' effectiveness in exposing performance problems in parallel and distributed programs.