ICLR 2018
The Sixth International Conference on Learning RepresentationsSchedule at a Glance
Overview Conference Papers Workshops Papers Invited Talks OralsMon Tue Wed Thu
General Chairs
- Yoshua Bengio, Université de Montreal
- Yann LeCun, New York University and Facebook
Senior Program Chair
- Tara Sainath, Google
Program Chairs
- Iain Murray, University of Edinburgh
- Marc’Aurelio Ranzato, Facebook
- Oriol Vinyals, Google DeepMind
Steering Committee
- Aaron Courville, Université de Montreal
- Hugo Larochelle, Google
Contact
The organizers can be contacted here.
Area Chairs »Best Reviewers
Amir-massoud Farahmand, Andrew Owens, David Kale, George Philipp, Julien Cornebise, Michiel van de Panne, Tom Schaul, Yisong Yue
Overview
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 deep 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 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.
- 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
The program will include keynote presentations from invited speakers, oral presentations, and posters.