ZbyszynskiDiDonatoTanaka2019TheEffectofCo-adaptiveLearningFeedbackinInteractiveMachineLearning.pdf (1.51 MB)
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The Effect of Co-adaptive Learning & Feedback in Interactive Machine Learning

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conference contribution
posted on 26.05.2020, 11:20 by Michael Zbyszynski, Balandino Di Donato, Atau Tanaka
In this paper, we consider the effect of co-adaptive learning on the training and evaluation of real-time, interactive machine learning systems, referring to specific examples in our work on action-perception loops, feedback for virtual tasks, and training of regression and temporal models. Through these studies we have encountered challenges when designing and assessing expressive, multimodal interactive systems. We discuss those challenges to machine learning and human-computer interaction, proposing future directions and research.

Funding

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No 789825).

History

Citation

Zbyszynski, Michael; Di Donato, Balandino and Tanaka, Atau. 2019. ’The Effect of Co-adaptive Learning & Feedback in Interactive Machine Learning’. In: ACM CHI: Human-Centered Machine Learning Perspectives Workshop. Glasgow, United Kingdom 4 May 2019.

Source

ACM CHI: Human-Centered Machine Learning Perspectives Workshop

Version

AM (Accepted Manuscript)

Acceptance date

05/03/2019

Copyright date

2019

Spatial coverage

Glasgow, SCOTLAND

Temporal coverage: start date

04/05/2019

Temporal coverage: end date

04/05/2019

Language

en