arXiv:2606.00157v1 Announce Type: cross Abstract: We consider establishing the interpretability theory of deep learning through constructing a corresponding relationship between the renormalization group (RG) method in statistical physics and the training process of deep neural networks (DNNs). We have proved the constructed relationship using the one-dimensional Ising model as the input data. In this paper we generalize our results to the case of continuous input data, which is a necessary preparation for applying the corresponding framework to real-world data. To be representative, we consid
This academic paper extends previous theoretical work on AI interpretability, following ongoing research trends in explainable AI.
It contributes to fundamental research in AI, which is critical for long-term safety, trustworthiness, and broader adoption but has no immediate market or geopolitical implications.
This paper offers a theoretical generalization for interpreting deep neural networks, moving from simplified models to continuous input data, which is a step towards applying such frameworks to real-world scenarios.
- · AI researchers
- · Academic institutions
Increased understanding of deep learning interpretability on a theoretical level.
Potential for improved debugging and validation of AI models in the distant future.
Broader public and regulatory trust in AI systems due to enhanced transparency.
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