no code implementations • 25 Jan 2022 • Oscar López, Daniel M. Dunlavy, Richard B. Lehoucq
We propose a novel statistical inference methodology for multiway count data that is corrupted by false zeros that are indistinguishable from true zero counts.
no code implementations • 22 Sep 2020 • Alexis Cooper, Xin Zhou, Scott Heidbrink, Daniel M. Dunlavy
Software flaw detection using multimodal deep learning models has been demonstrated as a very competitive approach on benchmark problems.
no code implementations • 9 Sep 2020 • Scott Heidbrink, Kathryn N. Rodhouse, Daniel M. Dunlavy
We explore the use of multiple deep learning models for detecting flaws in software programs.
no code implementations • 13 Jun 2019 • Gary J Saavedra, Kathryn N. Rodhouse, Daniel M. Dunlavy, Philip W Kegelmeyer
Fuzzing has played an important role in improving software development and testing over the course of several decades.
1 code implementation • 21 May 2010 • Daniel M. Dunlavy, Tamara G. Kolda, Evrim Acar
We show how the well-known Katz method for link prediction can be extended to bipartite graphs and, moreover, approximated in a scalable way using a truncated singular value decomposition.
no code implementations • 12 May 2010 • Evrim Acar, Tamara G. Kolda, Daniel M. Dunlavy, Morten Morup
In the presence of missing data, CP can be formulated as a weighted least squares problem that models only the known entries.
Numerical Analysis Numerical Analysis Data Analysis, Statistics and Probability G.1.3; G.1.6