no code implementations • 4 Apr 2023 • Vineel Nagisetty, Laura Graves, Guanting Pan, Piyush Jha, Vijay Ganesh
This functionality sets CGDTest apart from other similar DNN testing tools since it allows users to specify logical constraints to test DNNs not only for $\ell_p$ ball-based adversarial robustness but, more importantly, includes richer properties such as disguised and flow adversarial constraints, as well as adversarial robustness in the NLP domain.
no code implementations • 29 Oct 2020 • Antonina Kolokolova, Mitchell Billard, Robert Bishop, Moustafa Elsisy, Zachary Northcott, Laura Graves, Vineel Nagisetty, Heather Patey
In this paper we present a method for algorithmic melody generation using a generative adversarial network without recurrent components.
no code implementations • 21 Oct 2020 • Laura Graves, Vineel Nagisetty, Vijay Ganesh
In this paper, we present two efficient methods that address this question of how a model owner or data holder may delete personal data from models in such a way that they may not be vulnerable to model inversion and membership inference attacks while maintaining model efficacy.
1 code implementation • 24 Feb 2020 • Vineel Nagisetty, Laura Graves, Joseph Scott, Vijay Ganesh
A potential weakness in GANs is that it requires a lot of data for successful training and data collection can be an expensive process.
no code implementations • ICLR 2020 • Mitchell Billard, Robert Bishop, Moustafa Elsisy, Laura Graves, Antonina Kolokolova, Vineel Nagisetty, Zachary Northcott, Heather Patey
In this paper we present a method for algorithmic melody generation using a generative adversarial network without recurrent components.