\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{An interpretable ensemble method for deep representation learning}



\author[1]{Kai Jiang}%
\author[2]{Zheli Xiong}%
\author[2]{Qichong Yang}%
\author[3]{Jianpeng Chen}%
\author[4]{Gang Chen}%
\affil[1]{Key Lab of Information System Requirement, Nanjing Research Institute of Electronics Engineering}%
\affil[2]{University of Science and Technology of China School of Data Science}%
\affil[3]{River Delta Information Intelligence Innovation Research Institute}%
\affil[4]{Yangtze River Delta Information Intelligence Innovation Research Institute}%


\vspace{-1em}



  
  \date{May 27, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Model ensemble is widely used in deep learning since it can balance the
variance and bias of complex models. The mainstream model ensemble
methods can be divided into ``implicit'' and ``explicit''. The
``implicit'' method obtains different models by randomly inactivating the
internal parameters in the complex structure of the deep learning model,
and these models are integrated by sharing parameters. However, these
methods lack flexibility because they can only ensemble homogeneous
models with the similar structure. While the ``explicit'' ensemble
method can fuse completely different heterogeneous model structures,
which significantly enhances the flexibility of model selection and makes
it possible to integrate more models with entirely different
perspectives. However, the explicit ensemble will face the challenge of
averaging the outputs, leading to a chaotic result. To this end,
researchers further proposed using knowledge distillation and
adversarial learning technologies to perform a nonlinear combination of
multiple heterogeneous models to obtain better ensemble performance,
however these require significant modifications to the training or testing
procedure and are computationally expensive compared to simply
averaging. In this paper, based on the linear combination assumption, we
propose an interpretable ensemble method for averaging model results
which is simple to implement, and conducting experiments on the
representation learning tasks of Computer Vision(CV) and Natural
Language Processing(NLP). The results show that our method is superior
to direct averaging results while retaining the practicality of direct
averaging.%
\end{abstract}%



\sloppy


\textbf{Hosted file}

\verb`An interpretable ensemble method for deep representation learning.docx`  available at \url{https://authorea.com/users/589939/articles/626562-an-interpretable-ensemble-method-for-deep-representation-learning}

\selectlanguage{english}
\FloatBarrier
\end{document}

