arXiv:2605.30860v1 Announce Type: cross Abstract: A central aim of deep learning theory is to characterize how neural networks make predictions in the regime of simultaneously large model and training set size. Since the limits of diverging number of model parameters and dataset size do not commute it is not clear a priori what limits exist. In this work, we shed new light on these questions by studying Bayesian inference in deep non-linear MLPs in the regime where the number of training samples ($P$), the input dimension ($N_0$), the hidden layer width ($N$), and the number of hidden layers (
The paper is published as part of ongoing fundamental research into the theoretical underpinnings of deep learning, seeking to characterize model behavior in complex regimes.
Understanding the theoretical limits and behavior of large neural networks is crucial for optimizing their design, predicting performance, and advancing AI capabilities.
This research contributes to a deeper mathematical understanding of neural network scaling, which could lead to more efficient or robust AI model development in the future.
- · AI researchers
- · Deep learning framework developers
- · Companies investing in advanced AI
Improved theoretical understanding of Bayesian inference in deep MLPs under specific scaling laws.
Potential for developing more theoretically grounded and thus more predictable and robust AI architectures.
Acceleration of AI model development through better guidance on scaling and generalization, reducing empirical trial-and-error.
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Read at arXiv cs.LG