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With the same platform, a connected logic gate network with 3 inputs and 4 outputs, a decimal parity checker, and a ternary INHIBIT logic gate, are fabricated.
Two modeling methods were chosen to simulate the behavior of the designed multi-enzymatic logic AND gate network: ODE continuous approach and HSim discrete approach.
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c Gated network, similar to an expert network.
A training gating network can be implemented in two ways, by training in parallel both the experts and gating networks or sequential learning by training the experts first and then training the gating network.
If we consider adding one gating network for weighting, it is necessary to calibrate measurements because the gating network itself is for predicting uncertainties.
The gating network is only trained on this epoch when expert networks training is resumed.
We use a gating network that is similar to or shallower than the expert networks.
For the gating network, we initialized network weights with pre-trained models from ImageNet.
3 The gating network is trained until the loss is stagnant.
The gating network scheme is primarily the same as the mixture expert scheme.
Therefore, for the gating network input, there are six channels for the first layer of convolution.
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