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[NN] Improved reading flow

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Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens
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neural-networks/seminarpaper.tex
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@ -215,7 +215,7 @@ maxnames=2
Autonomous robots need to adapt to new situations. They have a need to learn
for an entire life. In order to do this they need a second environmental feedback
loop that tells them when to learn.\cite{Toutounji2016}
loop that tells them when to learn\cite{Toutounji2016}.
The learning itself is also described as plasticity. In the context of this paper
the definition of synaptic plasticity given by Citri\cite{Citri2008} will be used.
@ -226,7 +226,9 @@ and backpropagation.
When a network has to adapt to new situations, it has to learn new tasks. Usually
the previously learned weights are largely forgotten. This phenomenon is called
catastrophic forgetting.\cite{French1999,McCloskey1989}
catastrophic forgetting\cite{French1999,McCloskey1989}. It is highly problematic,
because the weights encode the learning of a network. If they are forgotten or
rather overwritten the previously learned tasks cannot be fulfilled anymore.
Since catastrophic forgetting is a key problem for autonomous learning, it is
crucial to overcome it. In this paper I will present some approaches for
@ -236,8 +238,9 @@ catastrophic forgetting.
\section{Catastrophic Forgetting}
\label{sec:catastrophicforgetting}
This section presents the major research developments related to catastrophic
forgetting and explains what it actually is. It follows the review of French\cite{French1999}.
French\cite{French1999} did a review of the existing research about catastrophic
forgetting. The following paragraphs will follow this review and highlight the
major developments in research related to catastrophic forgetting.
McCloskey and Cohen\cite{McCloskey1989} originally discovered the problem of
catastrophic forgetting, which was referred to as catastrophic interference. This
@ -305,12 +308,13 @@ catastrophic forgetting at bay, has to be named.
\section{Plasticity}
\label{sec:plasticity}
Every neural network involves a learning aspect and hence plasticity, given our
Catastrophic forgetting requires learned weights that can be forgotten.
Every neural network learns and therefore deals with plasticity, given our
definition of it. In this section three approaches for plasticity using diffusion-based
neuromodulation are presented in more detail. Modulated Random Search and
Modulated Gaussian Walk are using linearly modulated neural networks. They
are taken from Toutounji and Pasemann\cite{Toutounji2016}. The third approach
was introduced by Velez and Clune\cite{Velez2017} uses diffusion-based neuromodulation
was introduced by Velez and Clune\cite{Velez2017} and uses diffusion-based neuromodulation
for localized learning hence the name of the subsection here.
\subsection{Modulated Random Search}
@ -413,7 +417,7 @@ to find different network topologies (structure and weights combined).
\subsection{Modulated Gaussian Walk}
\label{subsec:mgw}
Toutounji and Pasemann introduce the modulated gaussian walk. The key differences
The modulated gaussian walk is introduced by Toutounji and Pasemann. The key differences
start with the parameters. There is no maximum sensitivity for the neuromodulator
concentration. When a weight change occurs the new weight is not chosen randomly
but rather the difference to be added to the current weight is sampled from a
@ -473,10 +477,10 @@ by the other season. This results in a localized learning.
\section{Comparison regarding catastrophic forgetting}
\label{sec:comparison}
In this section the three presented approaches for plasticity are compared with
regard to their ability to mitigate or overcome catastrophic forgetting. For both
the modulated random search and the modulated gaussian walk this aspect was
analyzed in the experiments conducted by Toutounji and Pasemann\cite{Toutounji2016}.
Given the presentations of the three approaches it is interesting to compare
them with regard to their ability to mitigate or overcome catastrophic forgetting.
For both the modulated random search and the modulated gaussian walk this aspect
was analyzed in the experiments conducted by Toutounji and Pasemann\cite{Toutounji2016}.
Therefore the results of their work will be utilized for this comparison.
Velez and Clune\cite{Velez2017} introduced the presented approach of localized
learning to analyze its capability with respect to overcoming catastrophic