Titlebook: Neural Networks: Tricks of the Trade; Grégoire Montavon,Geneviève B. Orr,Klaus-Robert Mü Book 2012Latest edition Springer-Verlag Berlin He

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束以馬具

Avoiding Roundoff Error in Backpropagating Derivatives they are small but non-zero. This roundoff error is easily avoided with a simple programming trick which has a small memory overhead (one or two extra floating point numbers per unit) and an insignificant computational overhead.

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Solace

Fierce

Square Unit Augmented, Radially Extended, Multilayer Perceptronsture has the localized properties of an RBFN but does not suffer as badly from the curse of dimensionality. I refer to a network of this type as a SQuare Unit Augmented, Radially Extended, MultiLayer Perceptron (SQUARE-MLP or SMLP).

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VEIL

Transformation Invariance in Pattern Recognition – Tangent Distance and Tangent Propagationof tangent vectors, which compactly represent the essence of these transformation invariances, and two classes of algorithms, “tangent distance” and “tangent propagation”, which make use of these invariances to improve performance.

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Introductionly apply neural networks to difficult real world problems. Often these “tricks” are theoretically well motivated. Sometimes they are the result of trial and error. However, their most common link is that they are usually hidden in people’s heads or in the back pages of space-constrained conference p

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Speeding Learningomplexity of our algorithms and the size of our problems will always expand to consume all cycles available, regardless of the speed of ourmachines.Thus, there will never come a time when computational efficiency can or should be ignored. Besides, in the quest to find solutions faster, we also often