  Ensemble learning is a technique where multiple component learners are
combined through a protocol. We propose an Ensemble Neural Network (ENN) that
uses the combined latent-feature space of multiple neural network classifiers
to improve the representation of the network hypothesis. We apply this approach
to construct an ENN from Convolutional and Recurrent Neural Networks to
discriminate top-quark jets from QCD jets. Such ENN provides the flexibility to
improve the classification beyond simple prediction combining methods by
linking different sources of error correlations, hence improving the
representation between data and hypothesis. In combination with Bayesian
techniques, we show that it can reduce epistemic uncertainties and the entropy
of the hypothesis by simultaneously exploiting various kinematic correlations
of the system, which also makes the network less susceptible to a limitation in
training sample size.
