no code implementations • 20 Aug 2019 • Bishwaranjan Bhattacharjee, John R. Kender, Matthew Hill, Parijat Dube, Siyu Huo, Michael R. Glass, Brian Belgodere, Sharath Pankanti, Noel Codella, Patrick Watson
We use this measure, which we call "Predict To Learn" ("P2L"), in the two very different domains of images and semantic relations, where it predicts, from a set of "source" models, the one model most likely to produce effective transfer for training a given "target" model.
no code implementations • 30 Jul 2018 • Parijat Dube, Bishwaranjan Bhattacharjee, Elisabeth Petit-Bois, Matthew Hill
This is currently addressed by Transfer Learning where one learns the small data set as a transfer task from a larger source dataset.
no code implementations • 10 Mar 2018 • Youcheng Sun, Xiaowei Huang, Daniel Kroening, James Sharp, Matthew Hill, Rob Ashmore
In this paper, inspired by the MC/DC coverage criterion, we propose a family of four novel test criteria that are tailored to structural features of DNNs and their semantics.