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Demonstration
Real-time interactive sequence generation with Recurrent Neural Network ensembles
Memo Akten
The demonstration allows users to gesturally 'conduct' the generation of text. We propose a method of real-time continuous control and ‘steering’ of sequence generation using an ensemble of RNNs, dynamically altering the mixture weights of the models. We demonstrate the method using character based LSTM networks and a gestural interface allowing users to ‘conduct’ the generation of text.
Author Information
Memo Akten (Goldsmiths, University of London)
Artist working with computation as a medium, exploring collisions between nature, science, technology, culture, ethics, ritual, tradition and religion. Doing PhD at Goldsmiths UoL in artificial intelligence and expressive human-machine interaction - particularly realtime image and sound synthesis and expressive manipulation using deep learning.
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2018 : Poster Session 1 »
Evan Casey · Colin A Raffel · Jonathan Simon · Juncheng Li · Robert Saunders · Petra Gemeinboeck · Eunsu Kang · Songwei Ge · Curtis Hawthorne · Anna Huang · Ting-Wei Su · Eric Chu · Memo Akten · Sonam Damani · Khyatti Gupta · Dilpreet Singh · Patrick Hutchings -
2016 : Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks »
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