Neural Compression @ICLR 2021
Accepted papers: see OpenReview site.
Link to ICLR virtual site: https://iclr.cc/virtual/2021/workshop/2130.
Data compression is a problem of great practical importance and a new frontier for machine learning research that combines empirical findings with fundamental theoretical insights. The goal of this workshop is to bring together researchers from deep learning, information theory, and probabilistic modeling in oder to learn from each other and to encourage exchange on fundamentally novel issues related to neural data compression.
Important update: The entire ICLR conference and all workshops have been pushed up by one day. The workshop is now scheduled for Friday, May 7, 2021.
The workshop solicits work related to neural data compression ranging from information-theoretical aspects to solving issues related to applications.
Submissions are invited for topics on, but not limited to:
- Image/Video/Audio Compression with AutoEncoders, Flows, AutoRegressive models, Generative Adversarial networks, etc.
- Neural Network Compression
- Probabilistic Modeling and Variational Inference for Compression
- Entropy Coding
- Minimal Description Length Theory
- Information Theory and Source Coding
- Submission deadline:
Feb 26, 2021extended to Feb 28, 2021 (11:59pm anywhere on earth)
- Notification data: March 26, 2021
- New Workshop date:
May 8, 2021May 7, 2021 (please note that ICLR has been pushed up by one day)
We solicit short workshop paper submissions of up to 4 pages + unlimited references/appendices. Please format submissions in ICLR style. Submissions will be double blind: reviewers cannot see author names when conducting reviews, and authors cannot see reviewer names.
Some accepted papers will be accepted as contributed talks. All accepted papers will be given a slot in the poster presentation session and published via Openreview after the workshop.
|Rianne van den Berg
Senior Research Scientist, Google
University of Texas at Austin
Vrije Universiteit Amsterdam
Co-founder and chair of MPEG; MPAI
VP, Core Media Technology/Engineering Fellow, Disney Streaming Services
University of California, Irvine
University of Tübingen, Germany
Imperial College London, UK
Disney Research Studios
University of Amsterdam; Qualcomm AI Research
Qualcomm AI Research
Qualcomm AI Research