arXiv:2605.03601v2 Announce Type: replace Abstract: We study the realization map of deep ReLU networks, focusing on when a function determines its parameters up to scaling and permutation. To analyze hidden redundancies beyond these standard symmetries, we introduce a framework based on weighted polyhedral complexes. Our main result shows that for every architecture whose input and hidden layers have width at least two, there exists an open set of identifiable parameters. This implies that the functional dimension of every such architecture is exactly the number of parameters minus the number
The paper was published on arXiv, representing a new academic finding in the ongoing research into deep learning architectures and their theoretical underpinnings.
This research provides deeper theoretical understanding of ReLU networks, which could lead to more robust and efficient AI model development by ensuring parameters are unique and interpretable.
The identification of conditions where ReLU network parameters are uniquely identifiable could improve model debugging, transfer learning, and the development of more stable AI systems.
- · AI researchers
- · Deep learning developers
- · AI software companies
Improved theoretical understanding of neural network behavior.
Development of more predictable and reliable AI models, reducing training complexity and improving interpretability.
Accelerated progress in AI applications where reliability and parameter interpretability are critical, such as safety-critical systems or general-purpose AI.
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Read at arXiv cs.LG