  This paper presents a novel method of searching for boosted hadronically
decaying objects by treating them as anomalous elements of a contaminated
dataset. A Variational Recurrent Neural Network (VRNN) is used to model jets as
sequences of constituent four-vectors. After applying a pre-processing method
which boosts each jet to the same reference mass and energy, the VRNN provides
each jet an Anomaly Score that distinguishes between the structure of signal
and background jets. The model is trained in an entirely unsupervised setting
and without high level variables, making the score more robust against mass and
$p_{T}$ correlations when compared to methods based primarily on jet
substructure. Performance is evaluated on the jet level, as well as in an
analysis context by searching for a heavy resonance with a final state of two
boosted jets. The Anomaly Score shows consistent performance along a wide range
of signal contamination amounts, for both two and three-pronged jet
substructure hypotheses. Analysis results demonstrate that the use of Anomaly
Score as a classifier enhances signal sensitivity while retaining a smoothly
falling background jet mass distribution. The model's discriminatory
performance resulting from an unsupervised training scenario opens up the
possibility to train directly on data without a pre-defined signal hypothesis.
