How to Write an IEEE Paper in 10 Minutes (No Setup)

\documentclass[conference]{IEEEtran}

% Essential packages
\usepackage{amsmath}
\usepackage{graphicx}
\usepackage{cite}
\usepackage[colorlinks=false]{hyperref}

\begin{document}

% ── TITLE ─────────────────────────────────
\title{Your Paper Title: A Descriptive Subtitle\\
  That Fits on Two Lines if Necessary}

\author{
  \IEEEauthorblockN{First Author}
  \IEEEauthorblockA{Department of Engineering\\
    University Name, City, Country\\
    email@university.edu}
  \and
  \IEEEauthorblockN{Second Author}
  \IEEEauthorblockA{Another Institution\\
    City, Country\\
    second@email.com}
}

\maketitle

% ── ABSTRACT ──────────────────────────────
\begin{abstract}
Write a 150--250 word summary of your paper here.
State the problem, your approach, key results, and
main conclusions. The abstract should be self-contained
and readable without the rest of the paper.
\end{abstract}

% ── KEYWORDS ──────────────────────────────
\begin{IEEEkeywords}
keyword one, keyword two, keyword three, keyword four
\end{IEEEkeywords}

% ── SECTION I ─────────────────────────────
\section{Introduction}

Introduce the problem your work addresses. Explain why
it is important and what gap in the literature you fill.
State your contributions clearly, typically as a bulleted
list at the end of the introduction:

\begin{itemize}
  \item We propose a novel method for X.
  \item We evaluate the method on benchmark Y.
  \item We release our code and data publicly.
\end{itemize}

% ── SECTION II ────────────────────────────
\section{Related Work}

Survey prior work in your area. Group related papers
into sub-themes. Compare your approach to each group.
Cite using \cite{key} — for example, prior work
on this topic \cite{smith2023,jones2022} established
the baseline metrics we improve upon.

% ── SECTION III ───────────────────────────
\section{Methodology}

Describe your technical approach. Use subsections
for clarity:

\subsection{Problem Formulation}

Let $\mathcal{X} = \{x_1, x_2, \ldots, x_n\}$ denote
the input set. We aim to find a function
$f: \mathcal{X} \to \mathcal{Y}$ that minimises:

\begin{equation}
  \mathcal{L} = \frac{1}{n} \sum_{i=1}^{n}
    \ell\bigl(f(x_i),\, y_i\bigr)
  \label{eq:loss}
\end{equation}

\subsection{Proposed Approach}

Describe your method, referencing Eq.~\eqref{eq:loss}
as needed. Include a figure if it helps:

\begin{figure}[htbp]
  \centering
  % \includegraphics[width=\columnwidth]{diagram.pdf}
  % Replace the line above with your actual figure file.
  \rule{\columnwidth}{0.5pt}  % placeholder box
  \caption{Architecture of the proposed system.}
  \label{fig:arch}
\end{figure}

As shown in Fig.~\ref{fig:arch}, the system consists
of three components.

% ── SECTION IV ────────────────────────────
\section{Experiments}

\subsection{Experimental Setup}

Describe datasets, baselines you compare against,
evaluation metrics, and hardware/software configuration.

\subsection{Results}

\begin{table}[htbp]
  \caption{Comparison with Baseline Methods}
  \label{tab:results}
  \centering
  \begin{tabular}{lccc}
    \hline
    Method & Precision & Recall & F$_1$ \\
    \hline
    Baseline A & 0.82 & 0.79 & 0.80 \\
    Baseline B & 0.85 & 0.81 & 0.83 \\
    \textbf{Ours} & \textbf{0.91} & \textbf{0.88} & \textbf{0.89} \\
    \hline
  \end{tabular}
\end{table}

Table~\ref{tab:results} shows our method achieves
the best F$_1$ score across all baselines.

% ── SECTION V ─────────────────────────────
\section{Conclusion}

Summarise what you did, what you found, and what
future work directions are open. Keep this section
concise — 2 to 3 paragraphs is typical.

% ── BIBLIOGRAPHY ──────────────────────────
% Option A: BibTeX (recommended)
\bibliographystyle{IEEEtran}
\bibliography{references}   % references.bib file

% Option B: Manual entries
% \begin{thebibliography}{9}
% \bibitem{smith2023} J. Smith, \textit{Title},
%   in Proc. ICML, 2023, pp. 1--10.
% \end{thebibliography}

\end{document}

% references.bib

@inproceedings{smith2023,
  author    = {Smith, John and Doe, Jane},
  title     = {A Novel Approach to Deep Learning},
  booktitle = {Proc. IEEE Int. Conf. Machine Learning},
  year      = {2023},
  pages     = {1--10},
  doi       = {10.1109/ICML.2023.12345}
}

@article{jones2022,
  author  = {Jones, Alice},
  title   = {Transformer Architectures for NLP},
  journal = {IEEE Trans. Neural Netw. Learn. Syst.},
  year    = {2022},
  volume  = {33},
  number  = {4},
  pages   = {1500--1515}
}