Affinity Group Activities
Please visit the Diversity & Inclusion page for scheduled program and activities.
RegistrationRegister starting Jan 21 08 AM PST »Register 01/21 »
- Alexander Rush, Cornell Tech
Senior Program Chair
- Shakir Mohamed, DeepMind
- Dawn Song, UC Berkeley
- Kyunghyun Cho, NYU
- Martha White, University of Alberta
- Gabriel Synnaeve, Facebook AI Research
- Asja Fischer, Ruhr University Bochum
- Animashree Anandkumar - Cal Tech / NVidia
- Kevin Swersky - Google AI
The organizers can be contacted here.
|Workshops||Sun Apr 26th|
|Conference Sessions||Sun Apr 26th through Thu the 30th|
|Workshop Application Open||Sept. 4, 2019, noon *|
|Paper Submission deadline||Sept. 25, 2019, 8 a.m. *|
|Workshop Application Close||Oct. 25, 2019, 11 p.m. *|
|Paper Rebuttal/discussion ends||Nov. 15, 2019, 2 p.m. *|
|Workshop Proposal Notifications||Nov. 27, 2019, 2 p.m. *|
|Sponsor Portal Open||Dec. 17, 2019, 8 a.m. *|
|Paper Decision Notification||Dec. 19, 2019, 2 p.m. *|
|Registration Opens||Jan. 21, 2020, 8 a.m. *|
|Workshop Organizers Announce Decisions||Feb. 25, 2020, 6 p.m. *|
|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
- 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