Restricted Bayesian Neural Network
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Abstract (arXiv)
Modern deep learning tools are remarkably effective in addressing intricate problems. However, their operation as black-box models introduces increased uncertainty in predictions. Additionally, they contend with various challenges, including the need for substantial storage space in large networks, issues of overfitting, underfitting, vanishing gradients, and more. This study explores the concept of Bayesian Neural Networks, presenting a novel architecture designed to significantly alleviate the storage space complexity of a network. Furthermore, we introduce an algorithm adept at efficiently handling uncertainties, ensuring robust convergence values without becoming trapped in local optima, particularly when the objective function lacks perfect convexity.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 29 references that are in the map. First posted on arXiv 6 Mar 2024.
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7 of 29 references.
References · 7
- 2018Gradient Descent Finds Global Minima of Deep Neural Networkscited by 574
- 2017A Brief Survey of Deep Reinforcement Learningcited by 483
- 2017Regularizing Neural Networks by Penalizing Confident Output Distributionscited by 464
- 2020Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Userscited by 338
- 2021What Are Bayesian Neural Network Posteriors Really Like?cited by 308
- 2013Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optimacited by 66
- 2020The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabelingcited by 64
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