Deep Tree Echo State Networks
Claudio Gallicchio, Alessio Micheli · 2018
This work proposes a first study, through empirical assessment, of a deep recursive Neural Network (RecNN) architecture for tree structured data exploiting the efficient design of the Echo State Network (ESN) framework. Three benchmark tasks for trees allow us to assess the potentiality of the novel Deep Tree ESN (DeepTESN) model with respect to the shallow counterpart (Tree ESN) and literature results (including hidden tree Markov models and kernel based approaches) in different conditions and according to both efficiency and predictive performance.