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This is a Quantitative Study study.

September 30, 2025TechnologiesOpen Access

Transfer Entropy and O-Information to Detect Grokking in Tensor Network Multi-Class Classification Problems

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Grokking is observed when generalization sharply occurs after initial memorization in multi-class tasks.
Transfer entropy reveals causal relationships between outputs, showing significant shifts in information flow during training.
Investigation of entanglement entropy indicates distinct transitions correlating with grokking in different datasets.
High-order information dynamics are crucial for understanding behaviors in quantum-inspired learning systems.

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Analysis shows that transfer entropy reveals causal dependencies in training, indicating grokking dynamics in multi-class classification.

Pomarico et al. (2025) studied this question.

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