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About this paper
Andrey Grabovsky, Vitaly Vanchurin
This work introduces bio-inspired algorithms for neural networks that mimic programmed death (pruning) and replication (expansion) of neurons based on novel statistical efficiency metrics. These dynamic structural modifications are shown to enhance learning efficiency and assist in navigating the loss landscape to escape local minima.
Key insights
- Neuron efficiency can be reliably quantified through statistical dependencies, where less efficient neurons can be approximated well by others or have minimal impact on overall network signals.
- Replication strategies that encourage maximal independence between a 'parent' and 'child' neuron's initial weight distribution prove most effective for quickly improving network performance.
- Combining programmed death and replication provides a mechanism for discrete structural transformations that allow learning systems to dynamically optimize, explore broader parameter spaces, and effectively escape local minima.
Summary by alphaXiv.
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