We have posted videos for many of the talks that were lived streamed. These videos are linked into the schedule (see the Schedule section above). They are also collected here.
Affinity Group Activities
Please visit the Diversity & Inclusion link for scheduled program and activities.
- Tara Sainath, Google
Senior Program Chair
- Alexander Rush, Harvard University
- Sergey Levine, UC Berkeley
- Karen Livescu, TTI-Chicago
- Shakir Mohamed, Google DeepMind
- Been Kim, Google Brain
- Graham Taylor, University of Guelph / Vector Institute
- Alice Oh, KAIST
- Richard Zemel, University of Toronto / Vector Institute
The organizers can be contacted here.
|Conference Sessions||Mon May 6th through Thu the 9th|
|Early pricing before this date.||Mar 19 '19 04:59 PM PDT *|
|Last chance for a refund on registration fees||Apr 18 '19 04:59 PM PDT *|
|All dates »||* Dates above are in pacific time|
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