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, fromacademic 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
optimal transport
theoretical issues in deep learning
societal considerations of representation learning including fairness, safety, privacy, and interpretability, and explainability
visualization or interpretation of learned representations
