Accepted Posters, Spotlights and Orals
Affinity Group Activities
Please visit the Diversity & Inclusion page for scheduled program and activities.
To Register without Credit Card: Contact ICLR Registration for processing information.
2019 Organizing Committee:
- Alexander Rush, Cornell Tech
Senior Program Chair
- Shakir Mohamed, DeepMind
- Dawn Song, UC Berkeley
- Kyunghyun Cho, NYU & FAIR
- Martha White, University of Alberta
- Gabriel Synnaeve, Facebook AI Research
- Asja Fischer, Ruhr University Bochum
- Animashree Anandkumar - Cal Tech / NVidia
- Kevin Swersky - Google AI
- Timnit Gebru - Google Brain
- Esube Bekele - In-Q-Tel
The organizers can be contacted here.
|Workshops Only (1 day)||Sun Apr 26th|
|Conference & Workshop Sessions||Sun Apr 26th through Thu the 30th|
|Workshop Application Open||Sep 04 12:00 PM PDT *|
|Paper Submission deadline||Sep 25 08:00 AM PDT *|
|Workshop Application Close||Oct 25 11:00 PM PDT *|
|Paper Rebuttal/discussion ends||Nov 15 02:00 PM PST *|
|Workshop Proposal Notifications||Nov 27 02:00 PM PST *|
|Paper Decision Notification||Dec 19 02:00 PM PST *|
|Sponsor Portal Open||Dec 23 08:00 AM PST *|
|Registration Opens||Jan 21 08:00 AM PST *|
|VolunteerApplicationOpen||Jan 21 08:00 AM PST *|
|Workshop Organizers Announce Decisions||Feb 25 06:00 PM PST *|
|Registration Cancellation Refund Deadline||Apr 12 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
- 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