175 citations · 229 across the 2 of their papers we have counts for
5 papers
The learnability scaling of quantum states: restricted Boltzmann machines
Dan Sehayek, Anna Golubeva, Michael S. Albergo +3
Generative modeling with machine learning has provided a new perspective on the data-driven task of reconstructing quantum states from a set of qubit measurements. As increasingly…
Batch Normalization is a Cause of Adversarial Vulnerability
Angus Galloway, Anna Golubeva, Thomas Tanay +2
Batch normalization (batch norm) is often used in an attempt to stabilize and accelerate training in deep neural networks. In many cases it indeed decreases the number of parameter…
QuCumber: wavefunction reconstruction with neural networks
Matthew J. S. Beach, Isaac De Vlugt, Anna Golubeva +6
As we enter a new era of quantum technology, it is increasingly important to develop methods to aid in the accurate preparation of quantum states for a variety of materials, matter…
Adversarial Examples as an Input-Fault Tolerance Problem
Angus Galloway, Anna Golubeva, Graham W. Taylor
We analyze the adversarial examples problem in terms of a model's fault tolerance with respect to its input. Whereas previous work focuses on arbitrarily strict threat models, i.e.…
Machine learning vortices at the Kosterlitz-Thouless transition
Matthew J. S. Beach, Anna Golubeva, Roger G. Melko
Efficient and automated classification of phases from minimally processed data is one goal of machine learning in condensed matter and statistical physics. Supervised algorithms tr…