Search Results for author: Miranda C. N. Cheng

Found 4 papers, 1 papers with code

Learning Lattice Quantum Field Theories with Equivariant Continuous Flows

1 code implementation1 Jul 2022 Mathis Gerdes, Pim de Haan, Corrado Rainone, Roberto Bondesan, Miranda C. N. Cheng

We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer and incorporates the full symmetries of the problem.

BIG-bench Machine Learning

Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows

no code implementations6 Oct 2021 Pim de Haan, Corrado Rainone, Miranda C. N. Cheng, Roberto Bondesan

We propose a continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics.

BIG-bench Machine Learning

Entangled q-Convolutional Neural Nets

no code implementations6 Mar 2021 Vassilis Anagiannis, Miranda C. N. Cheng

In both our experiments on the MNIST and Fashion-MNIST datasets, we observe a distinct increase in both the left/right as well as the up/down bipartition entanglement entropy during training as the network learns the fine features of the data.

BIG-bench Machine Learning General Classification

Covariance in Physics and Convolutional Neural Networks

no code implementations6 Jun 2019 Miranda C. N. Cheng, Vassilis Anagiannis, Maurice Weiler, Pim de Haan, Taco S. Cohen, Max Welling

In this proceeding we give an overview of the idea of covariance (or equivariance) featured in the recent development of convolutional neural networks (CNNs).

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