  In this paper, we present Deep Extreme Feature Extraction (DEFE), a new
ensemble MVA method for searching $\tau^{+}\tau^{-}$ channel of Higgs bosons in
high energy physics. DEFE can be viewed as a deep ensemble learning scheme that
trains a strongly diverse set of neural feature learners without explicitly
encouraging diversity and penalizing correlations. This is achieved by adopting
an implicit neural controller (not involved in feedforward compuation) that
directly controls and distributes gradient flows from higher level deep
prediction network. Such model-independent controller results in that every
single local feature learned are used in the feature-to-output mapping stage,
avoiding the blind averaging of features. DEFE makes the ensembles 'deep' in
the sense that it allows deep post-process of these features that tries to
learn to select and abstract the ensemble of neural feature learners. With the
application of this model, a selection regions full of signal process can be
obtained through the training of a miniature collision events set. In
comparison of the Classic Deep Neural Network, DEFE shows a state-of-the-art
performance: the error rate has decreased by about 37\%, the accuracy has
broken through 90\% for the first time, along with the discovery significance
has reached a standard deviation of 6.0 $\sigma$. Experimental data shows that,
DEFE is able to train an ensemble of discriminative feature learners that
boosts the overperformance of final prediction.
