In ML research, "grokking" is not used as a synonym for "generalization"; rather, it names a sometimes-observed delayed‑generalization training phenomenon in which training and held‑out performance do not improve in tandem, and in which held‑out performance rises abruptly later.
We study grokking, the onset of generalization long after overfitting, in a classical ridge regression setting. We prove end-to-end grokking results for learning over-parameterized linear regression models using gradient descent with weight decay. Specifically, we prove that the following stages occur: (i) the model overfits the training data early during training; (ii) poor generalization ...
One of the most intriguing is the phenomenon of grokking, where neural networks exhibit surprisingly delayed generalization, achieving high performance on unseen data long after they have...
Grok may be the only English word that derives from Martian. Yes, we do mean the language of the planet Mars. No, we're not getting spacey; we've just ventured into the realm of science fiction. Grok was introduced in Robert A. Heinlein 's 1961 science fiction novel Stranger in a Strange Land.
Grokking forces us to reconsider established practices in training neural networks. It challenges the validity of early stopping criteria and suggests that a model appearing to overfit might actually be on the verge of discovering deeper patterns.
One such phenomenon is grokking. According to the Oxford English Dictionary, “to grok something” means “to understand something completely using your feelings rather than considering the facts.”
Grokking, or delayed generalization, is a phenomenon where generalization in a deep neural network (DNN) occurs long after achieving near zero training error. Previous studies have reported the occurrence of grokking in specific controlled settings, such as DNNs initialized with large-norm parameters or transformers trained on algorithmic datasets.
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