1 code implementation • 27 Jul 2020 • Joseph Enguehard, Dan Busbridge, Adam Bozson, Claire Woodcock, Nils Y. Hammerla
The modelling of Electronic Health Records (EHRs) has the potential to drive more efficient allocation of healthcare resources, enabling early intervention strategies and advancing personalised healthcare.
1 code implementation • NAACL 2019 • Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Nils Y. Hammerla
Importantly, we show that Pearson correlation is appropriate for some word vectors but not others.
2 code implementations • ICLR 2019 • Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Francesco Moramarco, Jack Flann, Nils Y. Hammerla
Recent literature suggests that averaged word vectors followed by simple post-processing outperform many deep learning methods on semantic textual similarity tasks.
2 code implementations • ICLR 2019 • Dan Busbridge, Dane Sherburn, Pietro Cavallo, Nils Y. Hammerla
We investigate Relational Graph Attention Networks, a class of models that extends non-relational graph attention mechanisms to incorporate relational information, opening up these methods to a wider variety of problems.
1 code implementation • ICLR 2018 • Vitalii Zhelezniak, Dan Busbridge, April Shen, Samuel L. Smith, Nils Y. Hammerla
Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks.
36 code implementations • 11 Apr 2018 • Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y. Hammerla, Bernhard Kainz, Ben Glocker, Daniel Rueckert
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
Ranked #1 on Pancreas Segmentation on CT-150
6 code implementations • 13 Feb 2017 • Samuel L. Smith, David H. P. Turban, Steven Hamblin, Nils Y. Hammerla
We introduce a novel "inverted softmax" for identifying translation pairs, with which we improve the precision @1 of Mikolov's original mapping from 34% to 43%, when translating a test set composed of both common and rare English words into Italian.
no code implementations • 29 Apr 2016 • Nils Y. Hammerla, Shane Halloran, Thomas Ploetz
Human activity recognition (HAR) in ubiquitous computing is beginning to adopt deep learning to substitute for well-established analysis techniques that rely on hand-crafted feature extraction and classification techniques.