Publication: A Theoretical Framework for Target Propagation
A Theoretical Framework for Target Propagation
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Meulemans, A., Carzaniga, F., Suykens, J., Sacramento, J., & Grewe, B. F. (2020, December 12). A Theoretical Framework for Target Propagation. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver.
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The success of deep learning, a brain-inspired form of AI, has sparked interest in understanding how the brain could similarly learn across multiple layers of neurons. However, the majority of biologically-plausible learning algorithms have not yet reached the performance of backpropagation (BP), nor are they built on strong theoretical foundations. Here, we analyze target propagation (TP), a popular but not yet fully understood alternative to BP, from the standpoint of mathematical optimization. Our theory shows that TP is closely re
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Meulemans, A., Carzaniga, F., Suykens, J., Sacramento, J., & Grewe, B. F. (2020, December 12). A Theoretical Framework for Target Propagation. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver.
