4 code implementations • 5 Aug 2020 • Alejandro F. Queiruga, N. Benjamin Erichson, Dane Taylor, Michael W. Mahoney
We first show that ResNets fail to be meaningful dynamical integrators in this richer sense.
1 code implementation • 12 Jun 2017 • William H. Weir, Scott Emmons, Ryan Gibson, Dane Taylor, Peter J. Mucha
We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics.
Social and Information Networks Physics and Society
2 code implementations • 3 Apr 2019 • Dane Taylor, Mason A. Porter, Peter J. Mucha
Characterizing the importances of nodes in social, biological, information and technological networks is a core topic for the network-science and data-science communities.
Social and Information Networks Physics and Society 05C82
no code implementations • 7 Jul 2015 • Natalie Stanley, Saray Shai, Dane Taylor, Peter J. Mucha
While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes.
no code implementations • 31 Jul 2020 • N. Benjamin Erichson, Dane Taylor, Qixuan Wu, Michael W. Mahoney
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 malware' (i. e., compromised weights and activation pathways).
no code implementations • 2 Nov 2020 • Bao Huynh, Haimonti Dutta, Dane Taylor
Here, we analyze the effect on $\tau_\epsilon$ of network community structure, which can arise when compute nodes/sensors are spatially clustered, for example.
no code implementations • 26 Feb 2022 • Ting Chang, Yingjie Hu, Dane Taylor, Brian M. Quigley
In this study, we propose to derive information about the alcohol outlet visits of the residents of different neighborhoods from anonymized mobile phone location data, and investigate whether the derived visits can help better predict DV at the neighborhood level.