- Improving deep generative models by using human facial expression responses to samples from the model as a training signal.
The neural network model was trained using a carefully selected training signal that contained all the relevant process and controller dynamics.
Neural network based control of a serial-link robotic manipulator is considered subject to a signal dependent noise (SDN) model corrupting the training signal.
In many ANC applications, an on-line secondary path modeling method using white noise as a training signal is required to ensure convergence of the system.
In the absence of a consistent theory for nonlinear systems an heuristic approach is proposed, based on three main subjects: selection of a suitable training signal, calculation of the network parameters and selection of the most appropriate network configuration.
For example, position bias in search rankings strongly influences how many clicks a result receives, so that directly using click data as a training signal in Learning-to-Rank (LTR) methods yields sub-optimal results.
After receiving the training signal from the source, the relays take turns sending a training signal to the destination.
Figure 1 illustrates an example of the optimal training signal design in this type of optimal training signal implementation.
After receiving the training signal from the source, the relay sends a training signal so that the destination can be synchronised.
The number of OFDM symbols for a training signal is set to M=4.
Using the Lagrange multiplier method, we derive the optimality conditions for training signal design.
