( Image credit: R-Transformer )
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Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory.
Ranked #2 on Music Modeling on Nottingham
In contrast with this general approach, this paper shows that Transformers can do even better for music modeling, when we improve the way a musical score is converted into the data fed to a Transformer model.
In this paper we compare different types of recurrent units in recurrent neural networks (RNNs).
Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales.
We show that training a neural network to predict a seemingly more complex sequence, with extra features included in the series being modelled, can improve overall model performance significantly.
Ranked #1 on Music Modeling on JSB Chorales