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of 31
pro vyhledávání: '"Hammerla, Nils Y."'
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. However, EHRs are challenging to model due t
Externí odkaz:
http://arxiv.org/abs/2007.13794
A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals and many clever tricks. By contrast, little attention has been devoted
Externí odkaz:
http://arxiv.org/abs/1905.07790
Autor:
Zhelezniak, Vitalii, Savkov, Aleksandar, Shen, April, Moramarco, Francesco, Flann, Jack, Hammerla, Nils Y.
Recent literature suggests that averaged word vectors followed by simple post-processing outperform many deep learning methods on semantic textual similarity tasks. Furthermore, when averaged word vectors are trained supervised on large corpora of pa
Externí odkaz:
http://arxiv.org/abs/1904.13264
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. A thorough evaluation of thes
Externí odkaz:
http://arxiv.org/abs/1904.05811
Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks. We provide a simple yet rigorous explanation for this behaviour by introducing the concept of an optimal representation space,
Externí odkaz:
http://arxiv.org/abs/1805.03435
Autor:
Oktay, Ozan, Schlemper, Jo, Folgoc, Loic Le, Lee, Matthew, Heinrich, Mattias, Misawa, Kazunari, Mori, Kensaku, McDonagh, Steven, Hammerla, Nils Y, Kainz, Bernhard, Glocker, Ben, Rueckert, Daniel
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while high
Externí odkaz:
http://arxiv.org/abs/1804.03999
Usually bilingual word vectors are trained "online". Mikolov et al. showed they can also be found "offline", whereby two pre-trained embeddings are aligned with a linear transformation, using dictionaries compiled from expert knowledge. In this work,
Externí odkaz:
http://arxiv.org/abs/1702.03859
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. From these isolated applic
Externí odkaz:
http://arxiv.org/abs/1604.08880
Autor:
Fisher, James M., Hammerla, Nils Y., Ploetz, Thomas, Andras, Peter, Rochester, Lynn, Walker, Richard W.
Publikováno v:
In Parkinsonism and Related Disorders December 2016 33:44-50
Research on actuated interfaces has shown that people respond in certain socialized ways to interfaces that exhibit autonomous behaviours. We wished to explore the elements of design that drive people to regard an autonomous, interactive system as a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=core_ac_uk__::d03c27cee6115341ad59ba38fefd6687
https://nrl.northumbria.ac.uk/id/eprint/35281/1/p349-nowacka.pdf
https://nrl.northumbria.ac.uk/id/eprint/35281/1/p349-nowacka.pdf