Deep Extreme Feature Extraction: New MVA Method for Searching Particles in High Energy Physics

03/24/2016
by   Chao Ma, et al.
0

In this paper, we present Deep Extreme Feature Extraction (DEFE), a new ensemble MVA method for searching τ^+τ^- 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 σ. Experimental data shows that, DEFE is able to train an ensemble of discriminative feature learners that boosts the overperformance of final prediction.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/29/2021

Neural Network Ensembles: Theory, Training, and the Importance of Explicit Diversity

Ensemble learning is a process by which multiple base learners are strat...
research
11/19/2015

Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks

Convolutional Neural Networks have achieved state-of-the-art performance...
research
01/26/2023

Joint Training of Deep Ensembles Fails Due to Learner Collusion

Ensembles of machine learning models have been well established as a pow...
research
04/05/2021

DexDeepFM: Ensemble Diversity Enhanced Extreme Deep Factorization Machine Model

Predicting user positive response (e.g., purchases and clicks) probabili...
research
07/24/2017

Feature Extraction via Recurrent Random Deep Ensembles and its Application in Gruop-level Happiness Estimation

This paper presents a novel ensemble framework to extract highly discrim...
research
01/02/2022

The DONUT Approach to EnsembleCombination Forecasting

This paper presents an ensemble forecasting method that shows strong res...
research
02/19/2019

Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning

Environmental air quality affects people's life, obtaining real-time and...

Please sign up or login with your details

Forgot password? Click here to reset