Erica Ye (School of Computer Science, University of St Andrews)
, Online: 2:00 PM, 01 Dec 2021

Abstract

Driven by the persuasiveness of sensing technologies in our everyday devices, activity recognition has been widely adopted in many applications, from smart home, personal healthcare, human-computer interaction, to name a few examples. Machine learning, and especially deep learning, has made significant progress in learning sensor features from raw sensing signals with high recognition accuracy. 

However, most techniques need to be trained on a large labelled dataset, which is often difficult to acquire. To tackle this challenge, we have designed several unsupervised domain adaptation techniques to enable transferring the activity knowledge from one environment to many other environments. In this talk, we will introduce our recent developed techniques, including variational Autoencoder-based feature space alignment, and bi-directional generative adversarial networks-based feature space transfer. 

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Magda Chudzinska (CREEM/SMRU Consulting)
, Online: 2:00 PM, 15 Dec 2021

Abstract to follow

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Alison Johnston (UNIVERSITY OF ST ANDREWS)
Seminar Room, The Observatory: 2:00 PM, 26 Jan 2022

Joint Seminar with Statistics Division

Abstract to follow

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Professor Kate Jones (UCL)
Seminar Room, The Observatory: 2:00 PM, 16 Mar 2022

Details to be confirmed

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Madan Oli ()
Seminar Room, The Observatory: 2:00 PM, 30 Mar 2022

Abstract to follow

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CREEM/Statistics PhD student talks (UNIVERSITY OF ST ANDREWS)
Seminar Room, The Observatory: 2:00 PM, 06 Apr 2022RefID: 2040 click to edit (admin only)

CREEM/Statistics PhD student talks (UNIVERSITY OF ST ANDREWS)
Seminar Room, The Observatory: 2:00 PM, 13 Apr 2022RefID: 2041 click to edit (admin only)

CREEM/Statistics PhD student talks (UNIVERSITY OF ST ANDREWS)
Seminar Room, The Observatory: 2:00 PM, 27 Apr 2022RefID: 2042 click to edit (admin only)