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  <subfield code="a">Wong2021_ATI</subfield> 
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<subfield code="p">TRANSP-OR</subfield>
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<subfield code="a">TALK</subfield>
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  <subfield code="a">Wong, Melvin</subfield> 
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<datafield tag="245" ind1="" ind2="">
<subfield code="a">
An overview of deep learning strategies for choice modelling: current solutions and future directions</subfield>
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<datafield tag="260" ind1="" ind2="">
<subfield code="c">2021</subfield>
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<datafield tag="711" ind1="2" ind2="">
<subfield code="a">
Bridging machine learning and behaviour models</subfield>
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Alan Turing Institute, Online</subfield>
<subfield code="d">November 08, 2021</subfield>
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<subfield code="a">
There have been a number of innovative approaches in utilizing deep learning strategies for choice modelling in recent years. These strategies, initially developed for deep neural networks to solve various computational and complexity problems, are now being applied to discrete choice modelling. In this 'quick-fire' presentation, I will present a brief overview of recent works which have incorporated deep learning methods into choice models, and subsequently, identify future challenges and opportunities for deep learning in choice modelling research.</subfield>
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