arXiv:2606.04476v1 Announce Type: new Abstract: In this paper, we study the gradient descent dynamics for jointly training both layers of a one-hidden-layer ReLU network to fit a linear target function. Concretely, we consider a realizable setting where inputs are drawn i.i.d. from a Gaussian distribution and labels follow a planted linear model. This stylized framework captures salient features of end-to-end training in inverse problems and certain auto-encoder models. Despite its apparent simplicity, the dynamics remain poorly understood, in part because the loss landscape contains multiple
This is a fundamental research paper within an established academic calendar, focusing on theoretical aspects of deep learning. It reflects ongoing, incremental progress in AI research.
For a strategic reader, this item offers no immediate or direct strategic relevance, as it concerns theoretical deep learning dynamics rather than application or broader implications.
No immediate change in market dynamics, geopolitical landscape, or technological applications results from this theoretical study.
Further understanding of the theoretical underpinnings of neural networks for academic researchers.
Potentially improved theoretical understanding could, in the very long term, inspire more robust or efficient AI architectures.
These foundational insights might eventually contribute to advancements in a wide array of AI applications, but this is highly speculative and distant.
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