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We present a method for training a deep neural network containing sinusoidal activation functions to fit to time-series data. Weights are initialized using a fast Fourier transform, then trained with regular-ization to improve generalization. A simple dynamic parameter tuning method is employed to adjust both the learning rate and regularization term, such(More)
We present Forward Bipartite Alignment (FBA), a method that aligns the topological structures of two neural networks. Neural networks are considered to be a black box, because neural networks have a complex model surface determined by their weights that combine attributes non-linearly. Two networks that make similar predictions on training data may still(More)
An important task for training a robot (virtual or real) is to estimate state. State includes the state of the robot and its environment. Images from digital cameras are commonly used to monitor the robot due to the rich information, and low-cost hardware. Neural networks excel at catagorizing images, and should prove powerful to estimate the state of the(More)
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