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Dear Colleagues,</p>
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In our paper we present Closed-Loop Deep Learning which combines the power of deep learning and classical control to create an efficient analogue multi-layered fast learning paradigm, for robotic navigation. At the heart of this algorithm is an error signal
that arises from a reflex which is delicately used to both drive the closed loop system and train the deep learner simultaneously. Through mathematical derivation in z-space we show how to implement back-propagation in a closed-loop system.</p>
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Preprint:</p>
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<a href="https://arxiv.org/abs/2001.02970" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" style="margin:0px">https://arxiv.org/abs/2001.02970</a></p>
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Neural Computation (final version):</p>
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<a href="https://www.mitpressjournals.org/doi/abs/10.1162/neco_a_01317?journalCode=neco" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" style="margin:0px">https://www.mitpressjournals.org/doi/abs/10.1162/neco_a_01317?journalCode=neco</a></p>
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Please do not hesitate to get in touch for a copy of the paper and to discuss your thoughts.</p>
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Warmest Regards,</p>
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Sama Daryanavard</p>
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Dr Bernd Porr</p>
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Biomedical Engineering Division, School of Engineering,</p>
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University of Glasgow, Glasgow G12 8QQ, U.K.</p>
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