Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Datasets
Greatest papers with code
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• locuslab/TCN
• 
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
LANGUAGE MODELLING MACHINE TRANSLATION MUSIC MODELING SEQUENTIAL IMAGE CLASSIFICATION
Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions
• YatingMusic/remi
• 
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.
R-Transformer: Recurrent Neural Network Enhanced Transformer
ICLR 2020
• DSE-MSU/R-transformer
•
Recurrent Neural Networks have long been the dominating choice for sequence modeling.
Ranked #1 on
Music Modeling
on Nottingham
LANGUAGE MODELLING MUSIC MODELING SEQUENTIAL IMAGE CLASSIFICATION
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• michaelfarrell76/End-To-End-Generative-Dialogue
• 
In this paper we compare different types of recurrent units in recurrent neural networks (RNNs).
Sequential Neural Models with Stochastic Layers
How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks?
LSTM: A Search Space Odyssey
Several variants of the Long Short-Term Memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995.
Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
ICLR 2019
• BShakhovsky/PolyphonicPianoTranscription
•
Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales.
Music Transformer
ICLR 2019
• scpark20/Music-GPT-2
•
This is impractical for long sequences such as musical compositions since their memory complexity for intermediate relative information is quadratic in the sequence length.
Ranked #2 on
Music Modeling
on JSB Chorales
Improving Polyphonic Music Models with Feature-Rich Encoding
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
Diagonal RNNs in Symbolic Music Modeling
In this paper, we propose a new Recurrent Neural Network (RNN) architecture.