An Improved Adaptive PID Optimizer with Enhanced Convergence and Stability for Deep Learning
arXiv:2605.21968v1 Announce Type: new Abstract: Optimization is essential in deep learning. The foundational method upon which most optimizers are built is momentum-based stochastic gradient descent. However, it suffers from two key drawbacks. First, it has noisy and varying gradients, and second, it has an overshoot phenomenon. To address noisy gradients, Adam was proposed, which remains the most widely used adaptive optimizer. To address the overshoot phenomenon, a control-theory-based PID optimizer was proposed. To tackle both the limitations within a single framework, several variants of A
The paper addresses ongoing challenges in optimizing deep learning models, building on previous approaches like Adam and PID optimizers, suggesting a continuous evolution in AI research.
Improved optimizers enhance the efficiency and stability of deep learning models, potentially making AI training faster, more reliable, and accessible for complex tasks.
This advancement offers a more robust method for training deep learning models, which could lead to better performance and reduced computational costs in AI development.
- · AI researchers
- · Deep learning developers
- · Cloud computing providers
- · Industries utilizing deep learning
- · Inefficient optimization methods
- · Researchers relying on less stable optimizers
More efficient and stable deep learning training enables faster development cycles for new AI applications.
Enhanced deep learning capabilities can lead to breakthroughs in various fields, from healthcare to autonomous systems.
The democratization of more robust AI development tools could accelerate the adoption and sophistication of AI across global industries, potentially impacting economic productivity.
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Read at arXiv cs.LG