Discovering and Characterizing Hidden Variables Using a Novel Neural Network Architecture: LO-Net

<table>FVDS (three-LO-net architecture): comparing output from L-net 3 and hidden variable <svg height="10.95" id="M191" style="vertical-align:-0.1254pt" version="1.1" viewbox="0 0 9.1374998 10.95" width="9.1374998" xmlns="http://www.w3.org/2000/svg">
<g transform="matrix(1.25,0,0,-1.25,0,10.95)">
<g transform="translate(72,-63.24)">
<text transform="matrix(1,0,0,-1,-71.95,63.41)">
<tspan style="font-size: 12.50px; " x="0" y="0">ℎ</tspan>
</text>
</g>
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</svg>, correlation is 0.6808.</table>

Journal of Robotics

fig15

Figure 15

Figure 15: Discovering and Characterizing Hidden Variables Using a Novel Neural Network Architecture: LO-Net 