71 citations · 143 across the 34 of their papers we have counts for
4 papers · 2 filters
Continuous-in-Depth Neural Networks
Alejandro F. Queiruga, N. Benjamin Erichson, Dane Taylor +1
Recent work has attempted to interpret residual networks (ResNets) as one step of a forward Euler discretization of an ordinary differential equation, focusing mainly on syntactic…
Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware
N. Benjamin Erichson, Dane Taylor, Qixuan Wu +1
The ubiquity of deep neural networks (DNNs), cloud-based training, and transfer learning is giving rise to a new cybersecurity frontier in which unsecure DNNs have `structural malw…
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification
Francisco Utrera, Evan Kravitz, N. Benjamin Erichson +2
Transfer learning has emerged as a powerful methodology for adapting pre-trained deep neural networks on image recognition tasks to new domains. This process consists of taking a n…
Lipschitz Recurrent Neural Networks
N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga +2
Viewing recurrent neural networks (RNNs) as continuous-time dynamical systems, we propose a recurrent unit that describes the hidden state's evolution with two parts: a well-unders…