How Controlling the Variance can Improve Training Stability of Sparsely Activated DNNs and CNNs
arXiv:2602.05779v2 Announce Type: replace Abstract: The Edge-of-Chaos (EoC) theory developed for the random initialization of deep networks allows more efficient training by both preserving information in the initial outputs of the network and minimising exploding or vanishing gradients through characterisation of the intermediate layers as Gaussian processes. This EoC theory provides formulae for the choice of the initialisation distribution variances of the weights and biases. For activations which are approximately linear around the origin, the EoC theory typically encourages the Gaussian p
The paper addresses a critical technical challenge in deep learning (training stability of sparsely activated networks) that becomes more pronounced with the increasing complexity and scale of AI models.
Improved training stability directly translates to more efficient and reliable development of advanced AI, potentially accelerating progress in various applications and reducing computational resource waste.
The foundational understanding of how to initialize and stabilize complex neural networks is refined, offering practical guidance for researchers and practitioners to build more robust AI systems.
- · AI researchers and developers
- · Cloud computing providers
- · Deep learning hardware manufacturers
- · AI-driven industries
- · Inefficient AI development pipelines
- · Models reliant on brute-force hyperparameter tuning
More stable and efficient training of large-scale deep neural networks becomes possible.
This efficiency can lead to faster iteration cycles for AI development and potentially more powerful deployed AI models.
Reduced computational costs and increased reliability could democratize access to advanced AI development and usage, fostering broader innovation.
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Read at arXiv cs.LG