arXiv:2606.13818v1 Announce Type: new Abstract: This thesis investigates how Bayesian principles can deepen our understanding of modern deep learning systems. While neural networks achieve remarkable predictive performance, their ability to generalize and to quantify uncertainty remains only partly understood. This thesis approaches this challenge from both methodological and theoretical angles: unifying Bayesian inference, function-space modeling, and large-deviation theory under a common probabilistic perspective. On the methodological side, the thesis introduces the Deep Variational Implici
The thesis addresses fundamental challenges within modern deep learning, especially as AI systems become more ubiquitous and require greater transparency and reliability.
Improved uncertainty quantification and generalization in deep learning are critical for deploying AI in sensitive applications and for advancing the field beyond its current empirical success.
This theoretical and methodological work could lead to more robust, interpretable, and trustworthy AI model development, subtly shifting research directions and application potential.
- · AI researchers
- · Deep learning practitioners
- · High-stakes AI applications
- · Black-box AI models
- · Ad-hoc uncertainty quantification methods
Deep learning models will exhibit greater transparency in their predictions and associated confidence levels.
This improved reliability will accelerate the adoption of AI in regulated industries, such as healthcare and autonomous systems.
Increased trust in AI's decision-making could lead to broader societal integration, fostering new human-AI collaboration paradigms.
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Read at arXiv cs.LG