A few interesting papers submitted to ICLR 2019 around generative models for music. Coupled Recurrent Models for Polyphonic Music Composition Adversarial Audio Synthesis HAPPIER: Hierarchical Polyphonic Music Generative RNN GANSynth: Adversarial Neural Audio Synthesis Synthnet: Learning synthesizers end-to-end Autoencoder-based Music Translation Music Transformer Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset Modulated Variational Auto-Encoders for Many-to-Many Musical Timbre Transfer TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer Will update if I come across more :)

Slides for the tutorial I gave at the Re-Work Deep Learning conference on applying deep learning to modelling graphs.

Audio recording and slides for a talk I gave at The impact of ML on Society as part of The Foundation for Science and Technology debates hosted by The Royal Society.

Slides for my presentation on applications of Machine Learning for Drug Discovery at London Machine Learning Meetup in October is up.

The topic of how to approach Machine Learning (ML) research projects has come up many times over the past several years speaking to many in the field and industry. One of the fundamental aspects of ML projects is that there are often larger risks and unknowns attached to these project compared to others that are being undertaken within your company’s engineering department. While we have made tremendous progress in ML technologies, modelling algorithms, platforms, and libraries, we are still very far from having what I call text book algorithms that would reduce the risk of implementing a project.