Search Results for author: Nils Y. Hammerla

Found 8 papers, 7 papers with code

Neural Temporal Point Processes For Modelling Electronic Health Records

1 code implementation27 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.

Point Processes

Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors

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.

Semantic Textual Similarity Sentence +2

Relational Graph Attention Networks

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.

Graph Attention

Offline bilingual word vectors, orthogonal transformations and the inverted softmax

6 code implementations13 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.

Translation

Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables

no code implementations29 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.

Human Activity Recognition

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