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Recurrent Neural Networks for Adaptive Feature Acquisition

Abstract : We propose to tackle the cost-sensitive learning problem, where each feature is associated to a particular acquisition cost. We propose a new model with the following key properties: (i) it acquires features in an adaptive way, (ii) features can be acquired per block (several at a time) so that this model can deal with high dimensional data, and (iii) it relies on representation-learning ideas. The effectiveness of this approach is demonstrated on several experiments considering a variety of datasets and with different cost settings.
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Contributor : Thierry Artières <>
Submitted on : Friday, September 14, 2018 - 9:59:08 AM
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Thierry Artières, Gabriella Contardo, Ludovic Denoyer. Recurrent Neural Networks for Adaptive Feature Acquisition. 23rd International Conference on Neural Information Processing (ICONIP 2016), Oct 2016, Kyoto, Japan. pp.591-599, ⟨10.1007%2F978-3-319-46675-0_65⟩. ⟨hal-01874158⟩



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