References of "Vecoven, Nicolas"
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See detailIntroducing neuromodulation in deep neural networks to learn adaptive behaviours
Vecoven, Nicolas ULiege; Ernst, Damien ULiege; Wehenkel, Antoine ULiege et al

E-print/Working paper (2019)

In this paper, we propose a new deep neural network architecture, called NMD net, that has been specifically designed to learn adaptive behaviours. This architecture exploits a biological mechanism called ... [more ▼]

In this paper, we propose a new deep neural network architecture, called NMD net, that has been specifically designed to learn adaptive behaviours. This architecture exploits a biological mechanism called neuromodulation that sustains adaptation in biological organisms. This architecture has been introduced in a deep-reinforcement learning architecture for interacting with Markov decision processes in a meta-reinforcement learning setting where the action space is continuous. The deep-reinforcement learning architecture is trained using an advantage actor-critic algorithm. Experiments are carried on several test problems. Results show that the neural network architecture with neuromodulation provides significantly better results than state-of-the-art recurrent neural networks which do not exploit this mechanism. [less ▲]

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See detailNets versus trees for feature ranking and gene network inference
Vecoven, Nicolas ULiege; Begon, Jean-Michel ULiege; Huynh-Thu, Vân Anh ULiege et al

E-print/Working paper (2017)

We propose to tackle the challenging problem of gene regulatory network inference, using variable importance measures derived from artifi cial neural networks (ANN). When combined with a L1-regularized ... [more ▼]

We propose to tackle the challenging problem of gene regulatory network inference, using variable importance measures derived from artifi cial neural networks (ANN). When combined with a L1-regularized selection layer, these measures allow ANN to be competitive with state of the art techniques for this problem based on random forests. [less ▲]

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