Deviation bound for non-causal machine learning
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Concentration inequality are widely used for analysing machines learning algorithms. However, current concentration inequalities cannot be applied to the most popular deep neural network, notably in NLP processing. This is mostly due to the non-causal nature of this data. In this paper, a framework for modelling non-causal random fields is provided. A McDiarmid-type concentration inequality is obtained for this framework. In order to do so, we introduce a local i.i.d approximation of the non-causal random field.
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