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Can Grokking Predict Emergent Reasoning Capabilities?

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Introduction to Grokking The term “grokking” originates from Robert A. Heinlein’s science fiction novel, “Stranger in a Strange Land,” where it described deep and intuitive understanding of a concept or system. In contemporary discourse, grokking has evolved to symbolize a profound grasp of complex systems, which is essential in various fields, including computer science, cognitive […]

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Understanding the Impact of Batch Size on Grokking Dynamics

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Introduction to Grokking Dynamics Grokking dynamics is a crucial concept in the field of computational learning, particularly when assessing how machine learning models evolve in their performance over time. It encapsulates the processes through which a model develops an understanding of the underlying patterns in data, eventually leading to improved predictive capabilities. The term ‘grok’

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Exploring the Rarity of Grokking in Natural Datasets

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Understanding Grokking Grokking is a term that has evolved over time, originating from the science fiction novel “Stranger in a Strange Land” by Robert A. Heinlein, published in 1961. The concept was introduced as a way to describe a profound, intuitive understanding of something, often implying a seamless integration between the observer and the observed.

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Can Weight Decay Speed Up Grokking Convergence?

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Introduction to Grokking The concept of grokking in the context of machine learning and neural networks refers to a deep, intuitive understanding of the underlying patterns within the data. Unlike traditional learning paradigms, where models may learn through superficial correlations, grokking involves a robust integration of knowledge that allows models to generalize effectively across various

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Understanding Phase Transition During Grokking

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Introduction to Grokking The term “grokking” originates from the science fiction novel “Stranger in a Strange Land” written by Robert A. Heinlein in 1961. In the novel, grokking is described as a deep, intuitive understanding of something, transcending mere intellectual comprehension. This profound level of understanding and insight resonates well within various fields, particularly cognitive

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Accelerating Grokking Through Curriculum Learning

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Introduction to Grokking The term “grokking” originates from Robert A. Heinlein’s science fiction novel, “Stranger in a Strange Land,” where it denotes a profound understanding or deep comprehension of a subject. In the realms of machine learning and cognitive processes, grokking extends this concept, symbolizing not just knowledge acquisition, but the ability to embody that

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Understanding the Need for Multiple Epochs in Grokking Algorithmic Data

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Introduction to Grokking and Epochs In the realm of machine learning, the term ‘grokking’ denotes a profound comprehension or grasp of data patterns and underlying structures. It extends beyond mere analysis, embodying an intuitive understanding of the intricacies of data behavior. Grokking becomes particularly significant when dealing with complex datasets, where conventional methods may falter

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Why Do Wide Networks Show Weaker Double Descent?

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Introduction to Double Descent In the field of machine learning, the concept of double descent has garnered significant attention due to its implications for model performance as complexity increases. Traditional views on model behavior typically revolved around the bias-variance tradeoff, a framework that delineates how increasing model capacity can lead to reduced bias but heightened

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Can NTK Theory Predict Double Descent in Transformers?

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Introduction to NTK Theory The Neural Tangent Kernel (NTK) theory has emerged as a significant concept in understanding the behavior of neural networks during the training process. At its core, NTK theory provides a framework to analyze how changes in the parameters of a neural network affect its output, particularly in the context of gradient

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Understanding Late Double Descent Through Feature Learning

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Introduction to Feature Learning and Double Descent Feature learning is a critical component of machine learning that involves the automatic extraction of features from raw data, which aids in enhancing the predictive performance of models. This process efficiently identifies the underlying patterns and structures within complex datasets, facilitating the development of more sophisticated machine learning

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