3 citations · 5 across the 8 of their papers we have counts for
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Stochastic Path Sampler For Lattice Field Theory
Shiyang Chen, Moxian Qian, Gert Aarts +2
In lattice field theory, target distributions are known only up to normalization, (\tildeπ(ϕ)\propto e^{-S(ϕ)}), while the partition function is intractable. Markov chain Monte Car…
Exploring Generative Networks for Manifolds with Non-Trivial Topology
Shiyang Chen, Gert Aarts, Biagio Lucini
The expressive power of neural networks in modelling non-trivial distributions can in principle be exploited to bypass topological freezing and critical slowing down in simulations…
Random Matrix Theory for Stochastic Gradient Descent
Chanju Park, Matteo Favoni, Biagio Lucini +1
Investigating the dynamics of learning in machine learning algorithms is of paramount importance for understanding how and why an approach may be successful. The tools of physics a…
Scalar field Restricted Boltzmann Machine as an ultraviolet regulator
Gert Aarts, Biagio Lucini, Chanju Park
Restricted Boltzmann Machines (RBMs) are well-known tools used in Machine Learning to learn probability distribution functions from data. We analyse RBMs with scalar fields on the…