arXiv:2402.00152v5 Announce Type: replace Abstract: Constructing the architecture of a neural network is a challenging pursuit for the machine learning community, and the dilemma of whether to go deeper or wider remains a persistent question. This paper explores a comparison between deeper neural networks (DeNNs) with a flexible number of layers and wider neural networks (WeNNs) with limited hidden layers, focusing on their optimal generalization error in Sobolev losses. Analytical investigations reveal that the architecture of a neural network can be significantly influenced by various factor
The paper provides new analytical insights into neural network architecture at a time when AI model development is rapidly advancing and optimization is critical.
Understanding the optimal architecture for neural networks, whether deeper or wider, can lead to more efficient and powerful AI models, impacting compute resource utilization and model performance.
This research provides a theoretical framework to guide architectural choices, potentially shifting the empirical trial-and-error approach towards more principled design in neural networks.
- · AI researchers and developers
- · Hardware manufacturers (optimized compute)
- · Cloud AI service providers
- · Inefficient AI development practices
Improved efficiency in training and deploying neural networks.
Faster progress in AI capabilities due to better model designs.
Potentially reduced computational demands for achieving certain AI performance benchmarks.
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Read at arXiv cs.LG