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What is Randomized Algorithms

Encyclopedia of Information Science and Technology, Second Edition
A probabilistic sampling technique for exploring a space combining learning theories and Monte Carlo approaches.
Published in Chapter:
Robustness in Neural Networks
Cesare Alippi (Politecnico di Milano, Italy), Manuel Roveri (Politecnico di Milano, Italy), and Giovanni Vanini (Politecnico di Milano, Italy)
DOI: 10.4018/978-1-60566-026-4.ch527
The robustness analysis for neural networks aims at evaluating the influence on accuracy induced by perturbations affecting the computational flow; as such it allows the designer for estimating the resilience of the neural model w.r.t perturbations. In the literature, the robustness analysis of neural networks generally focuses on the effects of perturbations affecting biases and weights. The study of the network’s parameters is relevant both from the theoretical and the application point of view, since free parameters characterize the “knowledge space” of the neural model and, hence, its intrinsic functionality. A robustness analysis must also be taken into account when implementing a neural network (or the intelligent computational system into which a neural network is inserted) in a physical device or in intelligent wireless sensor networks. In these contexts, perturbations affecting the weights of a neural network abstract uncertainties such as finite precision representations, fluctuations of the parameters representing the weights in analog solutions (e.g., associated with the production process of a physical component), ageing effects or more complex, and subtle uncertainties in mixed implementations.
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