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The ubiquitous backpropagation algorithm requires sequential updates throughthe network introducing a locking problem. In addition, back-propagation relieson the transpose of forward weight matrices to compute updates, introducing aweight transport problem across the network. Locking and weight transport areproblems because they prevent efficient parallelization and horizontal scalingof the training process. We propose a new method to address both these problemsand scale up the training of large models. Our method works by dividing a deepneural network into blocks and introduces a feedback network that propagatesthe information from the targets backwards to provide auxiliary local losses.Forward and backward propagation can operate in parallel and with differentsets of weights, addressing the problems of locking and weight transport. Ourapproach derives from a statistical interpretation of training that treatsoutput activations of network blocks as parameters of probabilitydistributions. The resulting learning framework uses these parameters toevaluate the agreement between forward and backward information. Errorbackpropagation is then performed locally within each block, leading to"block-local" learning. Several previously proposed alternatives to errorbackpropagation emerge as special cases of our model. We present results on avariety of tasks and architectures, demonstrating state-of-the-art performanceusing block-local learning. These results provide a new principled frameworkfor training networks in a distributed setting.

Block-local learning with probabilistic latent representations