masterthesis-latex/glossary.tex

% acronyms
\newacronym{MC}{MC}{Monte Carlo}
\newacronym{MCDO}{MCDO}{Monte Carlo Dropout}
\newacronym{MCBN}{MCBN}{Monte Carlo Batch Normalisation}
\newacronym{MLP}{MLP}{multilayer perceptron}
\newacronym{NMS}{NMS}{non-maximum suppression}
\newacronym{OSE}{OSE}{open set error}
\newacronym{SSD}{SSD}{Single Shot MultiBox Detector}
\newacronym{pdf}{pdf}{probabilistic density function}

% terms
\newglossaryentry{BGR}{
    name={BGR},
    description={
        stands for the three colour channels blue, green, and red in this order
    }
}
\newglossaryentry{Caffe}{
    name={Caffe},
    description={
        is a deep learning framework written in C++
    }
}
\newglossaryentry{CCTV}{
    name={CCTV},
    description={
        stands for closed-circuit television or video surveillance
    }
}
\newglossaryentry{Dirichlet distribution}{
    name={Dirichlet distribution},
    description={
        is named after Peter Dirichlet and a family of probability
        distributions
    }
}
\newglossaryentry{entropy}{
    name={entropy},
    description={
        describes the amount of information provided by something. More likely
        events have a lower entropy than rare events. In case of classification probabilities, uniform predictions contain more information than predictions with a clear "winner"
    }
}
\newglossaryentry{Hopfield network}{
    name={Hopfield network},
    description={
        is a recurrent neural network. Used as "associative" memory
        systems with binary thresholds. Guaranteed to converge to local
        minimum, this can be the wrong one though
    }
}
\newglossaryentry{posterior}{
    name={posterior},
    description={
        probability output of a neural network
    }
}
\newglossaryentry{RGB}{
    name={RGB},
    description={
        stands for the three colour channels red, green, and blue in this order
    }
}
\newglossaryentry{vanilla}
{
    name={vanilla},
    description={
        is used to describe the original state of something
    }
}