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Laura Graves

4 papers hereh-index 3484 citations7 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • cs.SD1

identity via Semantic Scholar / OpenAlex

most citedxAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems

5 citations · 6 across the 3 of their papers we have counts for

collaborators

4 papers

cs.LG2023

CGDTest: A Constrained Gradient Descent Algorithm for Testing Neural Networks

Vineel Nagisetty, Laura Graves, Guanting Pan +2

In this paper, we propose a new Deep Neural Network (DNN) testing algorithm called the Constrained Gradient Descent (CGD) method, and an implementation we call CGDTest aimed at exp…

cs.SD2020★ 1 cited

GANs & Reels: Creating Irish Music using a Generative Adversarial Network

Antonina Kolokolova, Mitchell Billard, Robert Bishop +5

In this paper we present a method for algorithmic melody generation using a generative adversarial network without recurrent components. Music generation has been successfully done…

cs.LG2020

Amnesiac Machine Learning

Laura Graves, Vineel Nagisetty, Vijay Ganesh

The Right to be Forgotten is part of the recently enacted General Data Protection Regulation (GDPR) law that affects any data holder that has data on European Union residents. It g…

cs.LG2020★ 5 cited

xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems

Vineel Nagisetty, Laura Graves, Joseph Scott +1

Generative Adversarial Networks (GANs) are a revolutionary class of Deep Neural Networks (DNNs) that have been successfully used to generate realistic images, music, text, and othe…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.