Neural Networks' Grokking Transition Unveiled
Researchers have mapped the transition from memorization to generalization in neural networks, known as grokking. They found that doubling data accelerates generalization by about 4 times, while doubling width only improves performance by around 1.2 times. The study reveals a power-law scaling relation for generalization onset time: $T{ ext{grok}} ightarrow H^{-0.27} D^{-2.04} ext{eta}^{-0.50}…
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Key points
- Neural networks exhibit a delayed transition to generalization known as grokking
- Data complexity is the dominant factor in when this transition occurs
- Doubling data accelerates generalization by about 4 times
Read the original at arXiv cs.AI · by Anish Kataria primary sourceOpen source ↗
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