Natasha Jaques
Natasha Jaques
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Tuning Recurrent Neural Networks with Reinforcement Learning
Generating music using traditional supervised sequence models suffers from known failure modes, including the inability to produce coherent global structure. Music is an interesting sequence generation problem, because musical compositions adhere to known rules. We impose these rules with a novel algorithm combining RL and supervised learning.
Natasha Jaques
,
S. Gu
,
R. E. Turner
,
D. Eck
2016
In
International Conference on Learning Representations (ICLR) - workshop
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Magenta blog
MIT Tech Review article
Multi-task Multi-Kernel Learning for Estimating Individual Wellbeing
Wellbeing is a complex internal state consisting of several related dimensions, such as happiness, stress, energy, and health. We use Multi-task Multi-kernel learning to classify them simultaneously, leading to significant performance approvements.
Natasha Jaques
*
,
S. Taylor
*
,
A. Sano
,
R. Picard
2015
In
Neural Information Processing Systems (NeurIPS) Workshop on Multimodal Machine Learning
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