Search Results for author: Matthew Hill

Found 3 papers, 0 papers with code

P2L: Predicting Transfer Learning for Images and Semantic Relations

no code implementations20 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.

Transfer Learning

Improving Transferability of Deep Neural Networks

no code implementations30 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.

Small Data Image Classification Transfer Learning

Testing Deep Neural Networks

no code implementations10 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.

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