  In this paper, we propose a deep learning framework based on randomized
neural network. In particular, inspired by the principles of Random Vector
Functional Link (RVFL) network, we present a deep RVFL network (dRVFL) with
stacked layers. The parameters of the hidden layers of the dRVFL are randomly
generated within a suitable range and kept fixed while the output weights are
computed using the closed form solution as in a standard RVFL network. We also
propose an ensemble deep network (edRVFL) that can be regarded as a marriage of
ensemble learning with deep learning. Unlike traditional ensembling approaches
that require training several models independently from scratch, edRVFL is
obtained by training a single dRVFL network once. Both dRVFL and edRVFL
frameworks are generic and can be used with any RVFL variant. To illustrate
this, we integrate the deep learning networks with a recently proposed
sparse-pretrained RVFL (SP-RVFL). Extensive experiments on benchmark datasets
from diverse domains show the superior performance of our proposed deep RVFL
networks.
