We consider a broad range of subject areas including feature learning, metric learning, compositional modeling, structured prediction, reinforcement learning, and issues regarding large-scale learning and non-convex optimization, as well as applications in vision, speech recognition, text understanding, robotics, health care, sustainability, music, games, computational biology, and others.

A non-exhaustive list of relevant topics:

    unsupervised, semi-supervised, and supervised representation learning
    representation learning for planning and reinforcement learning
    representation learning for computer vision and natural language processing
    metric learning and kernel learning
    sparse coding and dimensionality expansion
    hierarchical models
    optimization for representation learning
    learning representations of outputs or states
    optimal transport
    theoretical issues in deep learning
    visualization or interpretation of learned representations
    implementation issues, parallelization, software platforms, hardware
    applications in audio, speech, robotics, neuroscience, computational biology, or any other field
    societal considerations of representation learning including fairness, safety, privacy, and interpretability