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Conditioned DFA (nDFA), a family that adapts established inverse-moment preconditioning to either side of this update, is studied, establishing practical benefits and important limits of conditioning learning with fixed random feedback.
Direct feedback alignment (DFA) trains hidden layers through fixed random projections of output error. With tanh hidden units and independent sigmoid outputs, plain stochastic gradient descent can stall near the loss of a constant predictor of class frequencies. We trace this stall to the error's common mode, the compo...
DendriNet, a trainable framework that varies integration rule, morphology, synaptic allocation, divisor locality, and dendritic nonlinearities is introduced, a trainable framework that varies integration rule, morphology, synaptic allocation, divisor locality, and dendritic nonlinearities.
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