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Introduction of PC-ALM: A Local Alternative to Backpropagation in Neural Networks
PC-ALM (Augmented Lagrangian Predictive Coding) is introduced as a method for training deep neural networks without relying on backpropagation. This approach utilizes layer-local dynamics and introduces dual neurons to enhance credit signal propagation. Experimental results show that PC-ALM can effectively train networks with up to 1000 layers, achieving performance comparable to traditional methods on various tasks.
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PC-ALM (Augmented Lagrangian Predictive Coding) is introduced as a method for training deep neural networks without relying on backpropagation. This approach utilizes layer-local dynamics and introduces dual neurons to enhance credit signal propagation. Experimental results show that PC-ALM can effectively train networks with up to 1000 layers, achieving performance comparable to traditional methods on various tasks.
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