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Ensemble learning is a technique where multiple component learners arecombined through a protocol. We propose an Ensemble Neural Network (ENN) thatuses the combined latent-feature space of multiple neural network classifiersto improve the representation of the network hypothesis. We apply this approachto construct an ENN from Convolutional and Recurrent Neural Networks todiscriminate top-quark jets from QCD jets. Such ENN provides the flexibility toimprove the classification beyond simple prediction combining methods bylinking different sources of error correlations, hence improving therepresentation between data and hypothesis. In combination with Bayesiantechniques, we show that it can reduce epistemic uncertainties and the entropyof the hypothesis by simultaneously exploiting various kinematic correlationsof the system, which also makes the network less susceptible to a limitation intraining sample size.

Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks