activity
20122022
most citedCovariance in Physics and Convolutional Neural Networks

12 citations · 30 across the 5 of their papers we have counts for

collaborators

10 papers

math.RT2022

Cone Vertex Algebras, Mock Theta Functions, and Umbral Moonshine Modules

Miranda C. N. Cheng, Gabriele Sgroi

We describe a family of indefinite theta functions of signature that can be expressed in terms of trace functions of vertex algebras built from cones in lattices. The famil…

cs.LG2021

Entangled q-Convolutional Neural Nets

Vassilis Anagiannis, Miranda C. N. Cheng

We introduce a machine learning model, the q-CNN model, sharing key features with convolutional neural networks and admitting a tensor network description. As examples, we apply q-…

math.NT2019

Three-Manifold Quantum Invariants and Mock Theta Functions

Miranda C. N. Cheng, Francesca Ferrari, Gabriele Sgroi

Mock modular forms have found applications in numerous branches of mathematical sciences since they were first introduced by Ramanujan nearly a century ago. In this proceeding we h…

cs.LG201912 cited

Covariance in Physics and Convolutional Neural Networks

Miranda C. N. Cheng, Vassilis Anagiannis, Maurice Weiler +3

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

hep-th2018

3d Modularity

Miranda C. N. Cheng, Sungbong Chun, Francesca Ferrari +2

We find and propose an explanation for a large variety of modularity-related symmetries in problems of 3-manifold topology and physics of 3d theories where such str…

hep-th2018

TASI Lectures on Moonshine

Vassilis Anagiannis, Miranda C. N. Cheng

The word moonshine refers to unexpected relations between the two distinct mathematical structures: finite group representations and modular objects. It is believed that the key to…