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pro vyhledávání: '"Roy, Hrittik"'
Bayesian deep learning all too often underfits so that the Bayesian prediction is less accurate than a simple point estimate. Uncertainty quantification then comes at the cost of accuracy. For linearized models, the null space of the generalized Gaus
Externí odkaz:
http://arxiv.org/abs/2410.16901
Autor:
Roy, Hrittik, Miani, Marco, Ek, Carl Henrik, Hennig, Philipp, Pförtner, Marvin, Tatzel, Lukas, Hauberg, Søren
Current approximate posteriors in Bayesian neural networks (BNNs) exhibit a crucial limitation: they fail to maintain invariance under reparameterization, i.e. BNNs assign different posterior densities to different parametrizations of identical funct
Externí odkaz:
http://arxiv.org/abs/2406.03334
Tuning scientific and probabilistic machine learning models $-$ for example, partial differential equations, Gaussian processes, or Bayesian neural networks $-$ often relies on evaluating functions of matrices whose size grows with the data set or th
Externí odkaz:
http://arxiv.org/abs/2405.17277
One of the main challenges in modern deep learning is to understand why such over-parameterized models perform so well when trained on finite data. A way to analyze this generalization concept is through the properties of the associated loss landscap
Externí odkaz:
http://arxiv.org/abs/2307.04719
Autor:
John S. Bak, Bill Reynolds
This cutting-edge research companion addresses our current understanding of literary journalism's global scope and evolution, offering an immersive study of how different nations have experimented with and perfected the narrative journalistic form/ge