7,735 research outputs found
Deep Tree Transductions - A Short Survey
The paper surveys recent extensions of the Long-Short Term Memory networks to
handle tree structures from the perspective of learning non-trivial forms of
isomorph structured transductions. It provides a discussion of modern TreeLSTM
models, showing the effect of the bias induced by the direction of tree
processing. An empirical analysis is performed on real-world benchmarks,
highlighting how there is no single model adequate to effectively approach all
transduction problems.Comment: To appear in the Proceedings of the 2019 INNS Big Data and Deep
Learning (INNSBDDL 2019). arXiv admin note: text overlap with
arXiv:1809.0909
Echo State Networks: analysis, training and predictive control
The goal of this paper is to investigate the theoretical properties, the
training algorithm, and the predictive control applications of Echo State
Networks (ESNs), a particular kind of Recurrent Neural Networks. First, a
condition guaranteeing incremetal global asymptotic stability is devised. Then,
a modified training algorithm allowing for dimensionality reduction of ESNs is
presented. Eventually, a model predictive controller is designed to solve the
tracking problem, relying on ESNs as the model of the system. Numerical results
concerning the predictive control of a nonlinear process for pH neutralization
confirm the effectiveness of the proposed algorithms for the identification,
dimensionality reduction, and the control design for ESNs.Comment: 6 pages,5 figures, submitted to European Control Conference (ECC
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