Glukhov, V. (2024). Permutative redundancy and uncertainty of the objective in deep learning. arXiv preprint arXiv:2411.07008.
This research paper investigates the impact of objective function uncertainty and permutative redundancy on the optimization of deep learning models, highlighting the limitations of traditional gradient-based approaches in such scenarios.
The author employs theoretical analysis and draws upon existing empirical studies on Hessian eigenvalues in deep networks to demonstrate how uncertainty in the objective function and the existence of numerous equivalent global optima affect the convergence of gradient descent methods.
The author argues that the traditional reliance on gradient-based optimization methods in deep learning may be inadequate, particularly for complex real-world problems with inherent data uncertainty. The paper advocates for exploring alternative optimization approaches and architectural modifications to address the challenges posed by objective function uncertainty and permutative redundancy.
This research highlights critical limitations of current deep learning optimization techniques, prompting a reevaluation of established practices and encouraging the development of more robust and efficient optimization strategies.
The paper primarily focuses on theoretical analysis and draws upon limited empirical evidence. Further research involving extensive experimentation with diverse deep learning architectures and datasets is necessary to validate the claims and explore the practical implications of the findings. Additionally, investigating and developing novel optimization algorithms and architectural modifications that mitigate the identified challenges are crucial areas for future work.
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by Vacslav Gluk... at arxiv.org 11-12-2024
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