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[NN] Created presentation slides for NN
Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
502
neural-networks/seminar_presentation.tex
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502
neural-networks/seminar_presentation.tex
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\RequirePackage{pdf14}
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\documentclass[14pt]{beamer}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Meta informations:
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\newcommand{\trauthor}{Jim Martens}
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\newcommand{\trtype}{Seminar} %{Proseminar} %{Seminar} %{Workshop}
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\newcommand{\trcourse}{Knowledge Processing with Neural Networks}
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\newcommand{\trtitle}{Catastrophic Forgetting and Neuromodulation}
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\newcommand{\trmatrikelnummer}{6420323}
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\newcommand{\tremail}{2martens@informatik.uni-hamburg.de}
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\newcommand{\trinstitute}{Dept. Informatik -- Knowledge Technology, WTM}
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\newcommand{\trwebsiteordate}{{http://www.informatik.uni-hamburg.de/WTM/}}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Languages:
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% Falls die Ausarbeitung in Deutsch erfolgt:
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% \usepackage[german]{babel}
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\usepackage[T1]{fontenc}
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\usepackage[utf8]{inputenc}
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% \usepackage[latin1]{inputenc}
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% \usepackage[latin9]{inputenc}
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% \selectlanguage{german}
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% If the thesis is written in English:
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Additional 'theorem' and 'definition' blocks:
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\newtheorem{axiom}{Axiom}[section]
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%\newtheorem{axiom}{Fakt}[section] % Wenn in Deutsch geschrieben wird.
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%Usage:%\begin{axiom}[optional description]%Main part%\end{fakt}
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%Additional types of axioms:
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\newtheorem{observation}[axiom]{Observation}
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%Additional types of definitions:
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\theoremstyle{remark}
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%\newtheorem{remark}[section]{Bemerkung} % Wenn in Deutsch geschrieben wird.
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\newtheorem{remark}[section]{Remark}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Provides TODOs within the margin:
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\newcommand{\TODO}[1]{\marginpar{\emph{\small{{\bf TODO: } #1}}}}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Abbreviations and mathematical symbols
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\newcommand{\RS}{\mathbb{R}}
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\newcommand{\erdos}{Erd\H{o}s}
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\newcommand{\renyi}{-R\'{e}nyi}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Display of TOCs:
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\AtBeginSection[]
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{
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\setcounter{tocdepth}{2}
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\begin{frame}
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\frametitle{Outline}
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\tableofcontents[currentsection]
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\end{frame}
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}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Document:
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\begin{document}
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\renewcommand{\arraystretch}{1.2}
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\begin{frame}[plain] % plain => kein Rahmen
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\titlepage
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\end{frame}
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%\setcounter{framenumber}{0}
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\begin{frame}[t]
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\frametitle{Motivation}
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\begin{itemize}
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\item robots need to learn continuously to adapt to new situations
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\vfill
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\item need to get feedback when they should learn (2nd environmental
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feedback loop)
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\vfill
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\item must not forget previously learned tasks
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\vfill
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\item therefore solution for catastrophic forgetting is required
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\end{itemize}
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\end{frame}
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\begin{frame}
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\frametitle{Outline}
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\tableofcontents
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\end{frame}
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%%%%%%%%%%%%%%
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% Your Content
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\section{Basics and Definition}
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\begin{frame}[t]
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\frametitle{Catastrophic Forgetting}
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\begin{itemize}
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\item networks completely forgets previously learned tasks
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\vfill
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\item originally discovered by McCloskey and
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Cohen\footnote{M. McCloskey and N. J. Cohen,
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"Catastrophic Forgetting in connectionist networks: The sequential
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learning problem"\cite{McCloskey1989}}
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\vfill
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\item radical example of "stability-plasticity" problem\cite{Grossberg1982}
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\end{itemize}
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\end{frame}
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\begin{frame}[t]
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\frametitle{Plasticity}
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\begin{itemize}
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\item learning is described as plasticity
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\vfill
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\item definition of synaptic plasticity given by
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Citri\footnote{A. Citri and R. C. Malenka, "Synaptic plasticity:
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Multiple forms, functions and mechanisms"\cite{Citri2008}} is used
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\vfill
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\item changing weights is already considered plasticity
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\end{itemize}
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\end{frame}
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\begin{frame}[t]
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\frametitle{Modulated Neural Network}
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\begin{itemize}
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\item any neural network with neuromodulator layer
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\vfill
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\item neuromodulator layer is 2nd environmental feedback loop
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\vfill
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\item Toutounji and Pasemann use neuromodulator cells (NMCs)
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\vfill
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\item spatial representation in the network
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\vfill
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\item production and reduction modes of NMCs
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\end{itemize}
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\end{frame}
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\begin{frame}[t]
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\frametitle{Linearly Modulated Neural Network\footnote{abbreviation: LMNN}}
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\begin{itemize}
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\item specific type of Modulated Neural Network (MNN)
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\vfill
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\item uses discrete time
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\vfill
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\item stimulates NMCs with linear model
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\vfill
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\item both random search and gaussian walk use this type of network
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\end{itemize}
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\end{frame}
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\section{Approaches}
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\begin{frame}[t]
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\frametitle{Modulated Random Search}
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\begin{itemize}
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\item random weight changes
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\vfill
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\item maximum weight change probability for each synapse
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\vfill
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\item weight change can happen any time
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\vfill
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\item new weight chosen randomly from given interval
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\vfill
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\item weight change probability is 2nd environmental feedback loop
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\end{itemize}
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\end{frame}
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\begin{frame}[t]
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\frametitle{Modulated Gaussian Walk}
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\begin{itemize}
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\item introduced by Toutounji and Pasemann\footnote{H. Toutounji and
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F. Pasemann, "Autonomous learning needs a second environmental feedback
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loop"\cite{Toutounji2016}}
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\vfill
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\item new weights are sum of old weight and value sampled from normal distribution
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\vfill
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\item distribution has mean of zero and \(\sigma^2\) variance
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\vfill
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\item sampled value can be infinitely large
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\vfill
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\item resampling until old weight + sampled value within interval
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\end{itemize}
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\end{frame}
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\begin{frame}[t]
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\frametitle{Localized Learning}
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\begin{itemize}
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\item introduced by Velez and Clune\footnote{R. Velez and J. Clune,
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"Diffusion-based neuromodulation can eliminate catastrophic forgetting
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in simple neural networks"\cite{Velez2017}}
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\vfill
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\item solves foraging task
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\begin{itemize}
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\item agent has lifetime of three years, each year has summer and winter
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\item in each season agent presented with food
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\item target is fitness value
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\end{itemize}
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\vfill
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\item initial weights from evolutionary algorithm
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\vfill
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\item two sources of neuromodulators (2nd environmental feedback loop)
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\end{itemize}
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\end{frame}
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\section{Results}
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\begin{frame}[t]
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\frametitle{Modulated Random Search}
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||||
\begin{itemize}
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||||
\item works well for positive light-tropism task
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\vfill
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\item everything else (obstacle avoidance or combination of both) does not
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work well
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\vfill
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\item intermediate temporary solutions significantly higher than final
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number of solutions
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\vfill
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\item even almost stable networks are destroyed if slightest weakness
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discovered
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\end{itemize}
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\end{frame}
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\begin{frame}[t]
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\frametitle{Modulated Gaussian Walk}
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\begin{itemize}
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||||
\item more likely to improve temporary solutions with weaknesses
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\vfill
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\item in combined task: twice as many temporary solutions that last longer
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||||
than 5 minutes
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||||
\vfill
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\item mitigates catastrophic forgetting but does not remove it
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||||
\end{itemize}
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\end{frame}
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\begin{frame}[t]
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\frametitle{Localized Learning}
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\begin{itemize}
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\item two functional modules formed
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\vfill
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||||
\item connections which learn in summer do not change in winter and vice
|
||||
versa
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||||
\vfill
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||||
\item completely removed catastrophic forgetting
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||||
\end{itemize}
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||||
\end{frame}
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\section{Conclusion}
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\begin{frame}[t]
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\frametitle{Conclusion}
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\begin{itemize}
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\item difference between random search and gaussian walk was learning rule
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||||
\vfill
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||||
\item localized learning uses Hebbian learning
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||||
\vfill
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||||
\item all use some form of diffusion-based neuromodulation
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||||
\end{itemize}
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||||
\end{frame}
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||||
\begin{frame}[t]
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\frametitle{Conclusion}
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||||
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||||
\begin{itemize}
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||||
\item random search is not useful to solve catastrophic forgetting
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||||
\vfill
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\item gaussian walk significantly reduces it
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||||
\vfill
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||||
\item localized learning solves it in very bespoke setup
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||||
\end{itemize}
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||||
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||||
Assumptions
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||||
\begin{itemize}
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||||
\item LMNN architecture likely better suited for more problems than
|
||||
sources architecture
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||||
\vfill
|
||||
\item Hebbian learning more useful for localized learning
|
||||
\vfill
|
||||
\item localized learning only works if feedback for all sub-tasks is
|
||||
available
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}[t]
|
||||
\frametitle{Conclusion}
|
||||
|
||||
\begin{itemize}
|
||||
\item random search is not useful to solve catastrophic forgetting
|
||||
\vfill
|
||||
\item gaussian walk significantly reduces it
|
||||
\vfill
|
||||
\item localized learning solves it in very bespoke setup
|
||||
\end{itemize}
|
||||
|
||||
Future work:
|
||||
\begin{itemize}
|
||||
\item comparison of LMNN architecture with "sources" architecture of
|
||||
localized learning
|
||||
\vfill
|
||||
\item comparison of Hebbian learning rule with gaussian walk learning rule
|
||||
\vfill
|
||||
\item researching applicability of localized learning to bigger problems
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
%%%%%%%%%%%%%%
|
||||
|
||||
%\begin{frame}[c]
|
||||
% \frametitle{The End}
|
||||
% \begin{center}
|
||||
% Thank you for your attention.\\[1ex]
|
||||
% Any question(s)?\\[5ex]
|
||||
% \end{center}
|
||||
%\end{frame}
|
||||
|
||||
\begin{frame}[allowframebreaks]{References}
|
||||
\printbibliography
|
||||
\end{frame}
|
||||
|
||||
\end{document}
|
||||
Reference in New Issue
Block a user