我打算在幻灯片中突出显示这两列之间的差异。尽管我使用了许多命令,但最终还是变得非常混乱\pause
。源代码包含在此处:
\frame{
\frametitle{Overview(contd.)}
\pause
\begin{columns}
\column{0.5\textwidth}
Unsupervised Text Embedding
\begin{itemize}
\item CBOW (Mikolov et al. 2013)
\item Skip-Gram (Mikolov et al. 2013)
\item Paragraph Vector (Le et al. 2014) \pause
\item Cons
\begin{itemize}
\item Fully unsupervised, not tuned for specific tasks
\end{itemize}
\item Pros
\begin{itemize}
\item Scalable, yet simple model
\item Insensitive parameters
\item Potential to leverage a large amount of unlabeled data, embeddings are general for different tasks
\end{itemize}
\end{itemize}
\column{0.5\textwidth}
(Deep) Neural Networks
\begin{itemize}
\item Recurrent Neural Networks (Mikolov et al. 2010)
\item Recursive Neural Networks (Socher et al. 2012)
\item Convolutional Neural Network(Kim et al. 2014) \pause
\item Pros :
\begin{itemize}
\item State-of-the-art performance on specific tasks
\end{itemize}
\item Cons :
\begin{itemize}
\item Computationally expensive
\item Require a large number of labeled data, hard to leverage unlabeled data
\item Very sensitive parameters, difficult to tune
\item Potential to leverage a large amount of unlabeled data, embeddings are general for different tasks
\end{itemize}
\end{itemize}
\end{columns}
}
答案1
不知道你的主题,无论是否使用\pause
命令,这里有几个选项。大多数情况下,我尝试将内容分成连贯的块,并将标题文本移动到实际的框架或部分标题。但无论你添加多少暂停,你给出的文本都快溢出幻灯片了。
下面我提供了两种不同的格式,根据主题和措辞,两种格式都可能合适。但是对于一系列包含相同内容类型且用于不同方法的幻灯片,我会尝试坚持使用一种特定的格式,并调整文本措辞以适应。
华沙主题,包括幻灯片顶部的部分标题空间:
默认主题,幻灯片顶部没有用于部分标题的空间:
文档:
\documentclass{beamer}
% Warsaw has document divisions at top, will need editing on slide 2 unless very few divisions
%\usetheme{Warsaw} \setbeamertemplate{navigation symbols}{}
% Default theme has no document divisions showing, can keep navigation symbols if desired
\usetheme{default}
\begin{document}
\section{Overview}
% Option 1: citations above, pros and cons in columns below
\begin{frame}
\frametitle{Unsupervised Text Editing}
\begin{itemize}
\item CBOW (Mikolov et al. 2013)
\item Skip-Gram (Mikolov et al. 2013)
\item Paragraph Vector (Le et al. 2014)
\end{itemize}
\begin{columns}[t]
\begin{column}{0.5\textwidth}
\begin{block}{Pros}
\begin{itemize}
\item Scalable, yet simple model
\item Insensitive parameters
\item Potential to leverage a large amount of unlabeled data, embeddings are general for different tasks
\end{itemize}
\end{block}
\end{column}
\begin{column}{0.5\textwidth}
\begin{block}{Cons}
\begin{itemize}
\item Fully unsupervised, not tuned for specific tasks
\end{itemize}
\end{block}
\end{column}
\end{columns}
\end{frame}
% Option 2: citations, pros, cons stacked without columns
\begin{frame}
\frametitle{(Deep) Neural Networks}
\begin{itemize}
\item Recurrent Neural Networks (Mikolov et al. 2010)
\item Recursive Neural Networks (Socher et al. 2012)
\item Convolutional Neural Network (Kim et al. 2014)
\end{itemize}
% \begin{columns}[t]
% \begin{column}{0.5\textwidth}
\begin{block}{Pros}
\begin{itemize}
\item State-of-the-art performance on specific tasks
\end{itemize}
\end{block}
% \end{column}
% \begin{column}{0.5\textwidth}
\begin{block}{Cons}
\begin{itemize}
\item Computationally expensive
\item Require a large number of labeled data, hard to leverage unlabeled data
\item Very sensitive parameters, difficult to tune
\item Potential to leverage a large amount of unlabeled data, embeddings are general for different tasks
\end{itemize}
\end{block}
% \end{column}
% \end{columns}
\end{frame}
\end{document}