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Oxford speeds neural networks for faster computer learning
The University of Oxford claims neural network can learn faster using a ‘feedback alignment’ algorithm, writes our technology editor Steve Bush.
It uses random feedback matrices to process errors update network parameters.
Multi-layer neural networks, inspired by the brain, are used for speech and image recognition within data sets.
Conventionally, these are trained using a ‘training set’ of inputs and expected outputs.
The difference between the expected and actual outputs is fed-back to adjust, and hopefully improve, the connection weightings of each layer.
However, “it would be impossible for the brain to implement the highly complex algorithms currently used to train these deep neural networks on a computer”, said Isis Innovation – the University’s intellectual property licencing company.
Understanding this has led to the feedback alignment algorithm, based on simpler circuitry requirements, and it is said to have had a number of unexpected benefits. For example, networks are trained quicker than through techniques such as ‘back propagation of errors’.
Steve BushTags: alignment algorithm, Isis Innovation, network parameters, neural network, Steve Bush