Exact(8)
McCulloch Pitts attractor networks minimizing probability flow can achieve robust exponential pattern storage.
Fig. 2 Learning critical networks with exponential memory by minimizing probability flow on few training patterns.
The authors develop policies based on the energy-error probability tradeoff to maximize successful transmission probability while minimizing probability of running out of energy.
EPCCW utilizes the physical layer (PHY) properties to create sub-slots for the purpose of further increasing reliability by both avoiding and minimizing probability of collision at slots that would nominally fail.
Although we have focused on minimizing probability flow to learn parameters in our discrete neural networks, several other strategies exist.
Fig. 3 Distribution of network parameters learned by minimizing probability flow (MPF) sharpens around three critical values.
Similar(51)
This in turn can minimize probability of blocking and probability of false network assignment.
This large neighborhood is used to minimize probability of unprocessed pixels after this stage.
We next analytically minimize probability flow to determine explicit networks achieving robust exponential storage.
The trade-off between minimizing the probability of blocking a new call and minimizing the probability of dropping users in handover is well known.
For example, one can utilize the expression for probability of outage to design a receiver that can optimization the system performance by minimizing the probability of outage.
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