Introduction

The submission deadline is in 3 hours.

Your ML breakthrough is ready, but your paper formatting is not meeting the requirements. Submit or miss another year?

This can happen in any conference cycle. Today, we prevent it from happening to you.

Learning Outcomes:

By the end of this session, you will:

Create a publication-ready research paper structure
Apply LaTeX formatting for a professional presentation

These skills determine whether your research gets accepted, cited, and trusted by the ML community.

Module 1: Research Paper Architecture

Every section of your paper must answer specific questions that reviewers ask. Think of your paper as a conversation with skeptical experts who need convincing.

Section	Key Problems/Focus	Aim For
🔥 Introduction	What problem are you solving? Why does it matter? How is your approach different?	"Compelling motivation"
🔬 Methods	How did you collect data? What analysis techniques? Can others replicate this?	"Rigorous reproducibility"
📊 Results	What concrete findings emerged? How do they address your research questions?	"Clear evidence"
🎯 Conclusion	What's the key takeaway? How does this advance the field? What are practical implications?	"Lasting impact"
⚠️ Limitations	What are honest constraints? What biases might exist? What couldn't you address?	"Honest transparency"

Detailed Section Guidelines

🔥 Introduction: Building Compelling Motivation

What is it: The introduction is your paper's first impression and often determines whether reviewers continue reading.

Context: Reviewers typically decide within the first two paragraphs whether a paper merits serious consideration. They're scanning for clear problem definition, significance, and novelty while evaluating hundreds of submissions under tight deadlines.

Why this matters: A weak introduction leads to immediate rejection, regardless of how brilliant your technical contribution might be. Reviewers need to quickly understand why your research matters to the broader ML community and how it advances current knowledge.

What to do: Structure your introduction using the "inverted pyramid" approach. Start with broad context about the field, then narrow to the specific problem you're addressing. Clearly articulate the gap in existing solutions and explain why current approaches are insufficient. Present your approach as a logical response to this gap, and conclude with explicit contributions (typically 3-4 bullet points stating exactly what you accomplished). Use concrete examples and avoid vague statements like "recent advances in deep learning."

🔬 Methods: Ensuring Rigorous Reproducibility

What is it: The methods section has evolved from simple description to detailed documentation that enables complete replication.

Context: Modern scientific publishing increasingly demands reproducibility as journals face pressure to combat the replication crisis. Reviewers are specifically trained to identify insufficient methodological detail that would prevent replication.

Why this matters: Irreproducible research wastes community resources and undermines scientific credibility. Many high-profile ML papers have been retracted or criticized for insufficient detail. Additionally, reproducibility demonstrates confidence in your approach and allows others to build upon your work.

What to do: Document every decision that could affect your results. Include dataset specifics (exact version, preprocessing steps, train/validation/test splits), model architecture details (layer sizes, activation functions, initialization schemes), training procedures (optimization algorithm, learning rate schedules, batch sizes), and computational environment (hardware specifications, software versions, random seeds). Write as if you're creating a recipe that a competent colleague could follow to recreate your exact results. When space is limited, provide essential details in the main text and comprehensive specifics in supplementary materials.

📊 Results: Presenting Clear Evidence

What is it: The results section synthesizes your raw findings into compelling evidence for your claims.

Context: Reviewers are trained to detect cherry-picked results, statistical manipulation, and presentation bias. They expect results that directly address your research questions with appropriate statistical rigor and honest reporting of both positive and negative findings.

Why this matters: This section proves whether your methodology actually works and answers your research questions. Weak results presentation can undermine even brilliant technical contributions. Conversely, clear, well-structured results can elevate modest contributions by demonstrating their practical value.

What to do: Organize results logically, typically from general performance to specific analyses. Start with overall model performance using standard metrics, then dive into detailed comparisons, ablation studies, and error analysis. Use clear visualizations with appropriate error bars, confidence intervals, or statistical significance tests. Report negative results honestly—they often provide valuable insights. For each major finding, explicitly connect it back to your original research questions. Include enough detail for readers to judge the practical significance of your improvements (e.g., "5% accuracy improvement" is more meaningful with context about baseline performance and dataset difficulty).

🎯 Conclusion: Creating Lasting Impact

What is it: The conclusion shapes how the research community understands and remembers your contribution.

Context: Busy researchers often read only the abstract, introduction, and conclusion before deciding whether to cite your work. This section determines whether your research influences future work or gets overlooked.

Why this matters: Your technical contribution only matters if others can understand its significance and apply it to their own work. The conclusion translates your specific findings into broader insights that advance the field.

What to do: Begin with a concise summary of your key findings (2-3 sentences maximum). Then explicitly state how these findings advance theoretical understanding or practical applications in ML. Discuss broader implications beyond your specific problem domain—how might your approach apply to related challenges? Suggest concrete directions for future research that build upon your work. Avoid introducing new information or overstating your contributions. Strike a balance between confidence in your results and humility about their scope.

⚠️ Limitations: Demonstrating Honest Transparency

What is it: Acknowledging limitations shows scientific maturity and helps readers appropriately interpret your findings.

Context: Experienced reviewers expect honest discussion of constraints and limitations. They view papers that ignore obvious limitations as naive or potentially deceptive, while thoughtful limitation discussions demonstrate research sophistication.

Why this matters: Every study has constraints, and attempting to hide them makes reviewers suspicious while reducing reader trust. Paradoxically, discussing limitations often strengthens your paper by showing thoughtful self-reflection and guiding appropriate application of your results.

What to do: Identify three types of limitations systematically. Scope limitations address generalizability—what populations, contexts, or problem types might your results not apply to? Methodological constraints include sample size issues, measurement limitations, or experimental design trade-offs. Potential biases might include dataset bias, researcher bias, or systematic errors in your approach. For each limitation, briefly explain its potential impact and suggest how future work could address it. Be honest without unnecessarily undermining your contributions—frame limitations as opportunities for future research rather than fatal flaws.

Quick Reference Framework

Title → Problem → Gap → Method → Findings → Impact → Limitations

This progression ensures logical flow and helps readers follow your research narrative from motivation through contribution to appropriate interpretation.

Module 2: Intro to LaTeX

What is LaTeX?

Think of LaTeX as a sophisticated word processor that works differently from Microsoft Word or Google Docs. Instead of clicking buttons to format text, you write commands that tell the computer how to format your document. This might seem harder at first, but it gives you precise control and is the standard for academic publications.

How LaTeX Works:

You write your content in a plain text file (like a .txt file) with special formatting commands. Then LaTeX "compiles" this file into a beautiful PDF. It's like giving instructions to a professional typesetter who formats your document perfectly.

Why Learn LaTeX for Academic Writing?

Professional appearance: LaTeX automatically handles spacing, fonts, and layout to meet academic standards
Mathematical notation: Essential for ML papers with equations and formulas
Reference management: Automatically formats citations and bibliographies
Industry standard: Most computer science conferences and journals expect LaTeX submissions

Getting Started: Your First LaTeX Document

Step 1: Setting Up:

Create an Overleaf account
Start with a template
Practice changing the title and adding simple text
Try compiling (clicking the "Recompile" button) to see your PDF

Start simple: You can write a complete research paper using just sections, paragraphs, figures, and basic citations. Advanced features can come later.

Step 2: Add structure

Create sections using \section{}
Add some paragraphs of text
Practice making text bold and italic
Compile frequently to see changes

Cheatsheet to get started:

Step3: Creating Sections and Subsections

latex

\section{Introduction} % Creates: 1. Introduction

\subsection{Background} % Creates: 1.1 Background

\subsubsection{Deep Learning} % Creates: 1.1.1 Deep Learning

Tip: Overleaf shows section structure in the left panel for easy navigation

Step 4: Adding Your Figures in Overleaf

Upload your image: Click "Upload" → select your .png, .jpg, or .pdf file
Insert the figure code:

latex

\begin{figure}[h] % [h] means "put the figure here if possible"

\centering % Centers the image

\includegraphics[width=0.8\textwidth]{your-filename.png} % Use exact filename

\caption{Describe what your figure shows}

\label{fig:results} % Give it a name for referencing

\end{figure}

% Reference it in your text

Figure~\ref{fig:results} shows that our method...

Note: Use the exact filename as it appears in your Overleaf file list

Step 5: Generate citations

How to Generate References with LaTeX (BibTeX)

When it comes to bibliography-management packages, you can use the biblatex package, to manage and format the bibliography in a LaTeX document.

A minimal working example of the biblatex package is shown below:

There are four bibliography-related commands in this example:

\usepackage{biblatex}

Imports the package biblatex.

\addbibresource{sample.bib}

Imports the bibliography data file sample.bib: this file contains information about each referenced book, article, etc. See the bibliography file section for more information.

\cite{einstein}

This command inserts a reference within the document, [1] in this case, that corresponds to an element in the bibliography, einstein is a keyword corresponding to an entry in sample.bib.

\printbibliography

Prints the list of cited references, the default title is References for the article document class and "Bibliography" for books and reports.

Creating a Bibliography File

The bibliography files must have the standard bibtex syntax:

This file contains records in a special format; for instance, the first bibliographic reference is defined by:

@article{...}

This is the first line of a record entry, @article tells BibTeX that the information stored here is about an article. The information about this entry is enclosed within braces. Besides the entry types shown in the example (article, book, online and inbook) there are a lot more, see the reference guide.

einstein

The label einstein is assigned to this entry, is a unique identifier that can be used to refer this article within the document.

author = "Albert Einstein",

This is the first field in the bibliography entry, indicates that the author of this article is Albert Einstein. Several comma-separated fields can be added using the same syntax key = value, for instance: title, pages, year, URL, etc. See the reference guide for a list of possible fields.

The information in this file can later be printed and referenced within a LaTeX document, as shown in the previous sections, with the command \addbibresource{sample.bib}. Not all the information in the .bib file will be displayed, it depends on the bibliography style set in the document.

Summary

Successful research publication requires integrating technical writing skills and professional formatting. Master these fundamentals early in your research career to avoid last-minute submission crises and build a foundation for impactful scientific communication.

The key is to view these skills not as separate requirements but as integrated components of responsible research practice. Every figure you create, every method you document, and every dataset you share contribute to the cumulative advancement of machine learning knowledge.