arXiv:2206.04359v3 Announce Type: replace Abstract: One of the fundamental challenges in the deep learning community is to theoretically understand how well a deep neural network generalizes to unseen data. However, current approaches often yield generalization bounds that are either too loose to be informative of the true generalization error or only valid to the compressed nets. In this study, we present a simple yet non-vacuous generalization bound from the optimization perspective. We achieve this goal by leveraging that the hypothesis set accessed by stochastic gradient algorithms is esse
The deep learning community is experiencing rapid advancements, making theoretical understanding of generalization a critical and timely pursuit to move beyond empirical successes.
Improved generalization bounds directly impact the reliability and trustworthiness of AI models, which is crucial for their deployment in high-stakes applications.
This research contributes to a foundation for developing AI models with more predictable real-world performance, potentially accelerating AI development and adoption in various sectors.
- · AI researchers
- · Deep learning practitioners
- · Industries deploying AI models (e.g., healthcare, finance)
- · Developers of overly complex or opaque AI models
- · Companies relying on 'black box' AI solutions without theoretical grounding
More robust and explainable AI models become possible.
Increased trust in AI systems could accelerate AI integration into critical infrastructure and decision-making processes.
A deeper theoretical understanding might unlock new AI architectures and learning paradigms currently constrained by empirical limitations.
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Read at arXiv cs.LG