Feedback Alignment Scalability Improved by Inducing Higher-Dimensional Updates

Researchers have identified a key limitation in Feedback Alignment (FA), a biologically plausible alternative to backpropagation, which hinders its scalability in deeper neural network architectures. They observed that FA's error signal has a lower rank than that of backpropagation, restricting its exploration of the parameter space. To address this, the study evaluated two methods—Muon optimizer and hidden activity normalization—which were found to significantly improve FA's performance on benchmarks like CIFAR100, increasing accuracy by up to 9 percentage points with a Resnet-18 model. AI

IMPACT Enhances the scalability of biologically plausible learning algorithms, potentially opening new avenues for neural network training.

RANK_REASON The cluster contains an academic paper detailing a new method for improving a machine learning algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Backpropagation
• CIFAR10
• CIFAR100
• Feedback Alignment
• Muon
• Resnet-18

• paper
• other

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