175 citations · 356 across the 11 of their papers we have counts for
11 papers
Dynamic Sparse Training with Structured Sparsity
Mike Lasby, Anna Golubeva, Utku Evci +2
Dynamic Sparse Training (DST) methods achieve state-of-the-art results in sparse neural network training, matching the generalization of dense models while enabling sparse training…
Bounding generalization error with input compression: An empirical study with infinite-width networks
Angus Galloway, Anna Golubeva, Mahmoud Salem +3
Estimating the Generalization Error (GE) of Deep Neural Networks (DNNs) is an important task that often relies on availability of held-out data. The ability to better predict GE ba…
Pruning a restricted Boltzmann machine for quantum state reconstruction
Anna Golubeva, Roger G. Melko
Restricted Boltzmann machines (RBMs) have proven to be a powerful tool for learning quantum wavefunction representations from qubit projective measurement data. Since the number of…
Are wider nets better given the same number of parameters?
Anna Golubeva, Behnam Neyshabur, Guy Gur-Ari
Empirical studies demonstrate that the performance of neural networks improves with increasing number of parameters. In most of these studies, the number of parameters is increased…
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…
Breaking of SU(4) symmetry and interplay between strongly correlated phases in the Hubbard model
A. Golubeva, A. Sotnikov, A. Cichy +2
We study the thermodynamic properties of four-component fermionic mixtures described by the Hubbard model using the dynamical mean-field-theory approach. Special attention is given…
Effects of anisotropy in simple lattice geometries on many-body properties of ultracold fermions in optical lattices
Anna Golubeva, Andrii Sotnikov, Walter Hofstetter
We study the effects of anisotropic hopping amplitudes on quantum phases of ultracold fermions in optical lattices described by the repulsive Fermi-Hubbard model. In particular, us…