Non-vacuous Generalization Bounds for Deep Neural Networks without any modification to the trained models
arXiv:2503.07325v2 Announce Type: replace Abstract: Understanding and certifying the behavior of modern deep neural networks remains a fundamental challenge in reliable machine learning. We introduce a new class of data-dependent generalization bounds that apply directly to trained models, without any modification. In particular, we present an exactly computable bound that is non-vacuous across all evaluated networks, including ImageNet-scale models with 600M parameters. This this is the first work showing that meaningful generalization guarantees are achievable even for large, unaltered deep
This paper addresses a long-standing challenge in reliable machine learning by offering directly applicable generalization bounds for large-scale deep neural networks, a crucial step for trust in AI systems.
Achieving verifiable and non-vacuous generalization bounds will increase the trustworthiness and deployability of advanced AI models, particularly in high-stakes environments, by providing guarantees on their out-of-sample performance.
The ability to certify the behavior of deep neural networks without modifying trained models changes the landscape of AI assurance, moving beyond empirical results towards theoretical guarantees, potentially accelerating adoption in regulated industries.
- · AI developers
- · High-assurance AI industries
- · Machine learning researchers
- · Ethical AI advocates
- · AI systems lacking transparency
- · Undifferentiated AI assurance vendors
Increased confidence in the reliability and safety of large AI models, fostering broader adoption.
Accelerated development of AI in critical infrastructure and regulated sectors due to enhanced trustworthiness.
Potential for new regulatory frameworks for AI that incorporate and mandate such generalization guarantees.
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