Plantilla LaTeX Tesis doctoral

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\centering
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{\color{white}\Large\bfseries Deep Learning for Scientific\\[8pt]
Document Understanding}\par
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\vspace{4cm}
{\large A.~J.~Martinez}\par
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{\normalsize\textcolor{subtext}
  A dissertation submitted in partial fulfilment of the requirements\\
  for the degree of \textit{Doctor of Philosophy}}\par
\vspace{0.8cm}
{\normalsize\textcolor{subtext} Department of Computer Science\\
  University of Cambridge}\par
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{\normalsize\textcolor{subtext} 2025}
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\frontmatter
\tableofcontents

\mainmatter

\chapter{Introduction}

The automatic understanding of scientific documents presents unique challenges at the intersection of natural language processing and mathematical reasoning. Modern transformer-based architectures have demonstrated remarkable ability to process structured text, yet the rich symbolic language of mathematics — with its equations, theorems, and logical dependencies — remains an open frontier.

This dissertation makes three primary contributions:
\begin{enumerate}
  \item A novel multimodal encoder for joint text and formula understanding.
  \item A large-scale benchmark of \num{100000} annotated scientific papers.
  \item State-of-the-art results on three downstream reasoning tasks.
\end{enumerate}

\chapter{Background}

\begin{definition}{Transformer Architecture}{transformer}
  A \emph{transformer} is a sequence-to-sequence model built entirely from
  self-attention and feed-forward layers, without recurrence.
  Given input embeddings $\mathbf{X}\in\mathbb{R}^{n\times d}$, the multi-head
  attention output is:
  \[
    \mathrm{Attention}(Q,K,V) = \mathrm{softmax}\!\left(\frac{QK^\top}{\sqrt{d_k}}\right)V.
  \]
\end{definition}

\begin{theorem}{Universal Approximation for Transformers}{universal}
  Transformers with sufficient depth and width can approximate any
  continuous sequence-to-sequence function on compact domains to
  arbitrary precision.
\end{theorem}

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