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\documentclass[12pt,fleqn]{beamer}
\input{beamerStyle.tex}
\input{abbrv.tex}
\title[Intro]{Course Overview}
\subtitle{Numerical Methods for Deep Learning}
\date{}
\begin{document}
\makebeamertitle
\section{Course Overview}
\label{sec:course_overview}
\begin{frame}
\frametitle{Part 1: Shallow Models}
\begin{enumerate}
\item Spectral Clustering for Unsupervised Learning
\begin{itemize}
\item Numerics: linear algebra, eigenvalue problems
\end{itemize}
\item Linear Least-Squares
\begin{itemize}
\item Numerics: QR, SVD, steepest descent, iterative solvers
\end{itemize}
\item Linear Classification
\begin{itemize}
\item Numerics: Convex optimization, Newton
\end{itemize}
\item Single Layer Neural Networks
\begin{itemize}
\item Reading/Presentation: \cite{HuangEtAl2006}
\item Numerics: Stochastic optimization, Gauss-Newton
\end{itemize}
\item Convolutional Neural Nets
\begin{itemize}
\item Reading/Presentation: \cite{LeCun1990,GlorotBengio2010}
\item Numerics: Structured matrix computation, PDE-based regularization
\end{itemize}
\end{enumerate}
\end{frame}
\begin{frame}
\frametitle{Part 2: Deep Models}
\begin{enumerate}
\setcounter{enumi}{5}
\item Deep Networks (Multilayer Perceptron)
\begin{itemize}
\item Reading/Presentation: \cite{Rumelhart1986}
\item Numerics: Backpropagation
\end{itemize}
\item Residual Neural Networks
\begin{itemize}
\item Reading/Presentation: \cite{he2016identity,he2016deep,E2017, ChenEtAl2018}
\item Numerics: ODE theory, time integrators
\end{itemize}
\item Optimal Control
\begin{itemize}
\item Reading/Presentation: \cite{GholamiEtAl2019}
\item Numerics: adjoint method, discretize-optimize
\end{itemize}
% \item Continuous Normalizing Flows
% \begin{itemize}
% \item Reading/Presentation: \cite{Zhang:2018th,Grathwohl:2018vf}
% \item Numerics: optimal transport, conservation laws
% \end{itemize}
\end{enumerate}
\end{frame}
\begin{frame}[allowframebreaks]
\frametitle{Articles for Student-led Presentations}
\bibliographystyle{unsrt}
\bibliography{NumDNN}
\end{frame}
% section course_overview (end)
\end{document}