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Two Tales of Single-Phase Contrastive Hebbian Learning
The search for "biologically plausible" learning algorithms has converged on the idea of representing gradients as activity differences.
AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks
We propose a new algorithm for training deep neural networks (DNNs) with binary weights.
Lifted Regression/Reconstruction Networks
In this work we propose lifted regression/reconstruction networks (LRRNs), which combine lifted neural networks with a guaranteed Lipschitz continuity property for the output layer.
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