Multiple Descents in Deep Learning as a Sequence of Order-Chaos Transitions in LSTM Networks
arXiv:2505.20030v2 Announce Type: replace-cross Abstract: We observe a novel `multiple-descent' phenomenon during the learning process of a recurrent neural network called long-short-term memory (LSTM) networks during its training on real-world task, in which the performance goes through long cycles of up and down trends multiple times after the model is overtrained. By carrying out asymptotic stability analysis of the models, we found that the cycles in performance -- indicated by loss function in test data -- are closely associated with the phase transition process between order and chaos of
This research provides a more granular understanding of deep learning training dynamics, specifically concerning LSTM networks, which are foundational for many sequential data tasks.
Understanding 'multiple descends' and order-chaos transitions can lead to more stable, efficient, and predictable deep learning model training, impacting the reliability and performance of AI systems.
This research suggests a more complex, cyclic performance landscape during deep learning Overtraining, implying that current assumptions about model convergence and generalization might need refinement.
- · AI researchers
- · Deep learning practitioners
- · Developers of sequential AI models
- · Inefficient AI training practices
- · Black-box optimization approaches
Improved understanding of LSTM training leads to more robust and higher-performing recurrent neural networks in practical applications.
New theoretical frameworks emerge for optimizing deep learning training, reducing computational waste and accelerating model development.
The principles discovered may extend to other complex adaptive systems, offering novel insights into their stability and dynamics.
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Read at arXiv cs.AI