
Underspecification Presents Challenges for Credibility in Modern Machine Learning
ML models often exhibit unexpectedly poor behavior when they are deploye...
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Understanding Double Descent Requires a FineGrained BiasVariance Decomposition
Classical learning theory suggests that the optimal generalization perfo...
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Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the InfiniteWidth Limit
Modern deep learning models have achieved great success in predictive ac...
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The Neural Tangent Kernel in High Dimensions: Triple Descent and a MultiScale Theory of Generalization
Modern deep learning models employ considerably more parameters than req...
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Cold Posteriors and Aleatoric Uncertainty
Recent work has observed that one can outperform exact inference in Baye...
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Finite Versus Infinite Neural Networks: an Empirical Study
We perform a careful, thorough, and large scale empirical study of the c...
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A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning
One of the distinguishing characteristics of modern deep learning system...
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Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks
We investigate under and overfitting in Generative Adversarial Networks ...
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Learning GANs and Ensembles Using Discrepancy
Generative adversarial networks (GANs) generate data based on minimizing...
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AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
AdaNet is a lightweight TensorFlowbased (Abadi et al., 2015) framework ...
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Ben Adlam
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