Alternatives to deep neural networks in finance
Two methods to approximate complex functions in an explainable way are presented
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Alexandre V. Antonov and Vladimir V. Piterbarg develop two methods for approximating slow-to-calculate functions and for conditional expected value calculations: the generalised stochastic sampling (gSS) method and the functional tensor train (fTT) method, respectively. These are proposed as high-performing alternatives to the generic deep neural networks (DNNs)
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