Understanding deep learning requires rethinking generalization pdf
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Introduction. Agenda. Understanding neural networks requires rethinking generalization. In fact, sheer memorization is possible to be effective for natural tasks. •Presenter: Hossein Through extensive systematic experiments, we show how the traditional approaches fail to explain why large neural networks generalize well in practice, and why understanding Understanding Deep Learning (Still) Requires Rethinking Generalization. Abstract Conventional wisdom attributes small generalization error either to properties of the model family, or to the regularization techniques used during training. •Authors: Zhang, Bengio, Hardt, Racht, Vinyals. Nonetheless, some of Traditional View of generalizationModel FamilyComplexity MeasuresRademacher ComplexityUniform StabilityVC dimensionRegularizationExplicit Understanding Deep Learning Requires Rethinking Generalization Contribution Traditional view of generalization is incapable of distinguishing between different Understanding Deep Learning Requires Rethinking Generalization Presentation by Rodney LaLonde at the University of Central Florida’s (UCF) Center for Research in Understanding Deep Learning (Still) Requires Rethinking Generalization. •ICLR, best papers award. (i, y i) i i min ω∈ℝd loss(,)n ∑ n i=1 ω Tx i y i d≥n w t+1 =w t −η te tx i t η t e t w=∑n i=1 α ix i w=XTα Effective UNDERSTANDING DEEP LEARNING REQUIRES RETHINKING GENERALIZATION (Chiyuan Zhang)ICLRFree download as PDF File.pdf), Text File.txt) or read All of the architectures use standard rectied linear activation functions (ReLU). By Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. Deep artificial neural networks often have far more trainable model parameters than the number of samples they are trained on. By Subas Rana and Afsaneh Shams. Through extensive Understanding Deep Learning Requires Rethinking Generalization. 1 INTRODUCTION. Research Question. For all experiments on CIFAR10, we train using SGD with a momentum parameter of An initial learning rate of (for small Inception) or (for small Alexnet and MLPs) are used, with a ay factor of per training epoch Computer Science > Machine Learning Title: Understanding deep learning requires rethinking generalization Authors: Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals The classical view of machine learning rests on the idea of parsimony.