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Found 7 papers, 3 papers with code
Graph-based Polyphonic Multitrack Music Generation
Graphs can be leveraged to model polyphonic multitrack symbolic music, where notes, chords and entire sections may be linked at different levels of the musical hierarchy by tonal and rhythmic relationships.
ChemAlgebra: Algebraic Reasoning on Chemical Reactions
Measuring the robustness of reasoning in machine learning models is challenging as one needs to provide a task that cannot be easily shortcut by exploiting spurious statistical correlations in the data, while operating on complex objects and constraints.
Modular Representations for Weak Disentanglement
However, at the moment, weak disentanglement can only be achieved by increasing the amount of supervision as the number of factors of variations of the data increase.
Leveraging Relational Information for Learning Weakly Disentangled Representations
This might be due, in part, to a formalization of the disentanglement problem that focuses too heavily on separating relevant factors of variation of the data in single isolated dimensions of the neural representation.
Calliope -- A Polyphonic Music Transformer
The polyphonic nature of music makes the application of deep learning to music modelling a challenging task.
ROS-Neuro Integration of Deep Convolutional Autoencoders for EEG Signal Compression in Real-time BCIs
Typical EEG-based BCI applications require the computation of complex functions over the noisy EEG channels to be carried out in an efficient way.
Learning Style-Aware Symbolic Music Representations by Adversarial Autoencoders
Through the paper, we show how Gaussian mixtures taking into account music metadata information can be used as an effective prior for the autoencoder latent space, introducing the first Music Adversarial Autoencoder (MusAE).
