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Poster De Conférence Année : 2014

Deep Sequential Neural Network

Résumé

Neural Networks sequentially build high-level features through their successive layers. We propose here a new neural network model where each layer is associated with a set of candidate mappings. When an input is processed, at each layer, one mapping among these candidates is selected according to a sequential decision process. The resulting model is structured according to a DAG like architecture, so that a path from the root to a leaf node defines a sequence of transformations. Instead of considering global transformations, like in classical multilayer networks, this model allows us for learning a set of local transformations. It is thus able to process data with different characteristics through specific sequences of such local transformations, increasing the expression power of this model w.r.t a classical multilayered network. The learning algorithm is inspired from policy gradient techniques coming from the reinforcement learning domain and is used here instead of the classical back-propagation based gradient descent techniques. Experiments on different datasets show the relevance of this approach.

Dates et versions

hal-01222608 , version 1 (30-10-2015)

Identifiants

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Ludovic Denoyer, Patrick Gallinari. Deep Sequential Neural Network. Deep Learning and Representation Learning Workshop, NIPS 2014, Dec 2014, Montreal, Canada. ⟨hal-01222608⟩
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