A new paper on arXiv explores the phenomenon of "grokking" in machine learning models, a process where models initially overfit training data but later generalize to new data. Researchers Baye Malick Gning and colleagues have developed an analytically tractable setting to study grokking, allowing them to isolate the effects of various factors like initialization, signal strength, and weight decay. Their work identifies specific conditions under which grokking is guaranteed to occur and provides analytical expressions for the duration of generalization plateaus, drawing parallels to chemical kinetics and fast/slow phases. AI
IMPACT Provides theoretical insights into model generalization, potentially informing future model development and training strategies.
RANK_REASON The cluster contains an academic paper detailing research findings on a machine learning phenomenon. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- Baye Malick Gning
- CatalyzeX
- DagsHub
- Gotit.pub
- Grokking Phenomenon
- Hugging Face
- IArxiv
- ScienceCast
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