4 hasil untuk "Alexander G Stapleton"
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Artikel Jurnal
AInstein: numerical Einstein metrics via machine learning
A new semi-supervised machine learning package is introduced which successfully solves the Euclidean vacuum Einstein equations with a cosmological constant, without any symmetry assumptions. The model architecture contains subnetworks for each patch in the manifold-defining atlas. Each subnetwork…
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Artikel Jurnal
Bayesian renormalization
In this note we present a fully information theoretic approach to renormalization inspired by Bayesian statistical inference, which we refer to as Bayesian renormalization. The main insight of Bayesian renormalization is that the Fisher metric defines a correlation length that plays the role of an…
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Artikel Jurnal
Bayesian RG flow in neural network field theories
The Neural Network Field Theory correspondence (NNFT) is a mapping from neural network (NN) architectures into the space of statistical field theories (SFTs). The Bayesian renormalization group (BRG) is an information-theoretic coarse graining scheme that generalizes the principles of the exact…
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Artikel Jurnal
Grokking vs learning: same features, different encodings
Grokking typically achieves similar losses to ordinary, ‘steady’, learning. This work asks whether these different learning paths lead to fundamental differences in the learned models. To do so, we compare the features, compressibility, and learning dynamics of models trained via each path in two…