The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning.

ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.

Participants at ICLR span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

A non-exhaustive list of relevant topics explored at the conference include:

    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
    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