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Why Do Subnetworks Generalize Better Than Dense Models?

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Introduction to Neural Network Architectures Neural networks have revolutionized the field of artificial intelligence, with various architectures designed to tackle different types of data and tasks. Two prominent architectures in this domain are dense models and subnetworks. Understanding how these neural network architectures function is essential to appreciating their strengths and weaknesses in terms of […]

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Why Do Subnetworks Generalize Better Than Dense Models?

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Introduction to Subnetworks and Dense Models In the realm of machine learning, particularly in neural networks, two key architectures play significant roles: subnetworks and dense models. A dense model, also referred to as a fully connected neural network, is characterized by its architecture wherein each neuron from one layer is connected to every neuron in

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Can Winning Tickets Be Found Without Full Training?

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Understanding the Basics of Winning Tickets Winning tickets are fundamentally tied to various forms of chance-based activities such as lotteries, gambling games, and competitive scenarios. The term ‘winning ticket’ commonly refers to entries or selections that achieve a prize, payout, or some form of recognition within these activities. In the realm of lotteries, a winning

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Scaling the Lottery Ticket Hypothesis to Transformers: Insights and Implications

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Introduction to the Lottery Ticket Hypothesis The Lottery Ticket Hypothesis is a pivotal concept in the realm of neural networks, first introduced by Frankle and Carbin in their seminal paper in 2019. This hypothesis posits that within a large neural network, there exists a smaller, efficient subnetwork – the so-called “winning ticket.” This winning ticket

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Understanding the Weaker Double Descent Phenomenon in Transformers Compared to MLPs

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Introduction to Double Descent Double descent is a fascinating phenomenon observed in modern machine learning, particularly relevant to its applications in deep learning architectures like transformers and multilayer perceptrons (MLPs). This concept describes a non-monotonic behavior of model performance as the complexity of the model increases, which can be characterized through the relationship between bias,

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Understanding Late Double Descent: The Role of Feature Learning

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Introduction to Double Descent The phenomenon known as double descent in machine learning has emerged as a significant area of study within the field. Traditionally, machine learning practitioners relied heavily on the bias-variance tradeoff to understand model performance. This tradeoff implies that as model complexity increases, bias decreases while variance increases, leading to a point

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Understanding NTK Theory and Its Role in Early Double Descent

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Introduction to NTK Theory and Double Descent The Neural Tangent Kernel (NTK) theory has emerged as a powerful framework for understanding the training dynamics of neural networks. It provides insightful perspectives on how networks behave during their learning process. At its core, NTK theory examines the linear approximation of neural networks in the infinite-width limit.

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Understanding the Decrease in Test Error After Interpolation Regime

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Introduction to Interpolation Regime Interpolation, in the context of machine learning and statistics, refers to the method of estimating unknown values that fall within the range of a discrete set of known data points. This concept is particularly crucial as it plays a vital role in model training and evaluation. In practice, interpolation is often

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Understanding Double Descent in Very Wide Networks

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Understanding Double Descent in Very Wide Networks Double descent is an intriguing phenomenon that has emerged in the field of machine learning, particularly in relation to neural networks. Traditionally, the performance of machine learning models has been assessed through the lens of the bias-variance trade-off. In this framework, increasing model complexity typically leads to improved

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How SAM Optimizer Finds Flatter Loss Landscapes

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Introduction to the SAM Optimizer The optimization of loss functions is a fundamental challenge in training deep learning models. Traditional optimizers, such as Stochastic Gradient Descent (SGD) and its variants, have historically focused on minimizing the loss without considering the stability of the optimization process. These methods operate by navigating through the loss landscape, which

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