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Semantic Representation Learning

Friday, 19 July 2019
11:00-17:00

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three circles inside a funnel each circle has a label, data cleansing, data mapping or trial conversions. there is an arrow omitting from the bottom of the funnel that says, data conversion.

The provisional timetable for the workshop is as follows:

11:00-11:10 Welcome

11:10-12:20 Shay Cohen (University of Edinburgh)

12:20-13:20 Lunch

13:20-14:30 Sebastian Riedel (UCL)

14:30-15:40 Thomas Lukasiewicz (University of Oxford)

15:40-16:00 Coffee break

16:00-17:00 Angelika Kimmig (Cardiff University)

Representation learning is the task of converting data from the raw form in which it is given into a form which is more suitable for machine learning models. Given the popularity of deep learning, the most common approach is to learn a mapping from data items onto a fixed-dimensional vector. Such vector representations are commonly used in image processing and natural language processing in particular. A particular challenge, however, is to learn vector representations which are, in some sense, semantically meaningful. On the one hand, this is critical for enabling machine learning models which are interpretable, a property that is quickly becoming critical in many applications. On the other hand, semantically meaningful representations are needed to incorporate existing domain knowledge (e.g. provided by a domain expert or available from some knowledge base), and thus to inject knowledge into machine learning models.

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C/2.07.
Queen's Buildings
5 The Parade
Newport Road
Cardiff
CF24 3AA

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