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von N Langlois — When using tile coding as a function approx- imator for high-dimensional reinforcement- learning problems, it is common to decom-. - all the outputs are self dependent. - Feed-forward nets don't remember historic input data at test time unlike recurrent networks. Page 14. Intern © Siemens05.07.2022 — In this work, we present a novel method to identify whether a specific feature is relevant to a classifier's decision or not. This relevance is von A Rössig · 2019 · Zitiert von: 2 — In this thesis we consider the problem of verifying linear properties of neural networks. Neural networks are employed succesfully for a broad range. von M Jahn · 2018 · Zitiert von: 18 — use of artificial neural network (ANN) regression models is given. It means that the objective function (here: the sum of squared. von A Minin · Zitiert von: 2 — alized to recurrent complex valued neural network in general. New substracts the bias and applies the activation function to the prod- To sum up,. 05.07.2022 — PDF | On Apr 1, 2007, M. Weygandt and others published Real-time fMRI A neural network may be composed of several layers of identical This paper describes parallel algorithms for training artifcial neural networks. Possible levels of parallelity are presented. Experiments for checking the Weighted sum. – Euclidian distance. • Transfer function f: – Threshold function. (McCulloch & Pitts) Perceptron as neural network. • Inputs are random.