to reusing the filter parameters in the convolution operations, while the element in the weight matrix of traditional neural networks are used only once to calculate the output.

### Long-Short Term Memory

Long Short Term Memory (LSTM) networks are a special kind of RNN, proposed by Hochreiter in 1997 [@hochreiter1997long]. LSTM units are a building unit for layers of a Recurrent Neural Network(RNN) [@LSTM]. A RNN composed of LSTM units are often called an LSTM network. LSTM are explicitly designed to avoid the long-term dependency problem. A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate. The cell is responsible for “remembering” values over arbitrary time intervals; hence LSTM is capable of learning long-term dependencies.

(ct) [$c_t$]{}; (int1) ; (x1) [$\times$]{}; (ht) [$h_t$]{}; (x2) [$\times$]{}; (int2) ; (x3) [$\times$]{}; (ft) [$f_t$]{}; (it) [$i_t$]{}; (ot) [$o_t$]{};

i/in [int2/x2, x2/ct, ct/int1, int1/x1, x1/ht, it/x2, ct/it, ct/ot, ot/x1, ft/x3]{} (i)–();

(ct) to\[bend right=45\] (ft);

(ct) to\[bend right=30\] (x3); (x3) to\[bend right=30\] (ct);

(fit) ;

(fit.west|-int2) coordinate (aux)–++(180:7mm) node\[left\][$x_t$]{}; (\[yshift=1mm\]aux)–++(135:7mm); (\[yshift=-1mm\]aux)–++(-135:7mm);

(fit.north-|it) coordinate (aux)–++(90:7mm) node\[above\][$x_t$]{}; (\[xshift=1mm\]aux)–++(45:7mm); (\[xshift=-1mm\]aux)–++(135:7mm);

(fit.north-|ot) coordinate (aux)–++(90:7mm) node\[above\][$x_t$]{}; (\[xshift=1mm\]aux)–++(45:7mm); (\[xshift=-1mm\]aux)–++(135:7mm);

(fit.south-|ft) coordinate (aux)–++(-90:7mm) node\[below\][$x_t$]{}; (\[xshift=1mm\]aux)–++(-45:7mm); (\[xshift=-1mm\]aux)–++(-135:7mm);

The expression long-short term refers to 