366 citations · 2.6k across the 128 of their papers we have counts for
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Taming numerical imprecision by adapting the KL divergence to negative probabilities
Simon Pfahler, Peter Georg, Rudolf Schill +4
The Kullback-Leibler (KL) divergence is frequently used in data science. For discrete distributions on large state spaces, approximations of probability vectors may result in a few…
Gauge-equivariant pooling layers for preconditioners in lattice QCD
Christoph Lehner, Tilo Wettig
We demonstrate that gauge-equivariant pooling and unpooling layers can perform as well as traditional restriction and prolongation layers in multigrid preconditioner models for lat…
Provenance for Lattice QCD workflows
Tanja Auge, Gunnar Bali, Meike Klettke +4
We present a provenance model for the generic workflow of numerical Lattice Quantum Chromodynamics (QCD) calculations, which constitute an important component of particle physics r…
Gauge-equivariant neural networks as preconditioners in lattice QCD
Christoph Lehner, Tilo Wettig
We demonstrate that a state-of-the art multi-grid preconditioner can be learned efficiently by gauge-equivariant neural networks. We show that the models require minimal re-trainin…