The performance of machine learning methods is heavily dependent on the choice of data representation (or features) on which they are applied. The rapidly developing field of representation learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data. We take a broad view of the field and include topics such as deep learning and feature learning, metric learning, compositional modeling, structured prediction, reinforcement learning, and issues regarding large-scale learning and non-convex optimization. The range of domains to which these techniques apply is also very broad, from vision to speech recognition, text understanding, gaming, music, etc.

A non-exhaustive list of relevant topics :

• Unsupervised, semi-supervised, and supervised representation learning
• Representation learning for planning and reinforcement learning
• 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 vision, audio, speech, natural language processing, robotics, neuroscience, or any other field