Poster
in
Workshop: Machine Learning and the Physical Sciences
Combinational-convolution for flow-based sampling algorithm
Akio Tomiya
Abstract:
We propose a new class of efficient layer called {\it CombiConv} (Combinational-convolution) that improves the acceptance rate for the flow-based sampling algorithm for quantum field theory on the lattice. CombiConv is made from a -dimensional convolution out of lower -dimensional convolutions and combining their outputs, and CombiConv has fewer parameters than the standard convolutions.We apply CombiConv to the flow-based sampling algorithm,Furthermore, we find that for every -dimensional scalar theory CombiConv for achieves a higher acceptance rate than others.
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