We invite researchers to submit work in any of the following areas:
- Uncertainty in deep learning,
- probabilistic deep models (such as extensions and application of Bayesian neural networks),
- deep probabilistic models (such as hierarchical Bayesian models and their applications),
- deep generative models (such as variational autoencoders),
- practical approximate inference techniques in Bayesian deep learning,
- connections between deep learning and Gaussian processes,
- applications of Bayesian deep learning,
- or any of the topics below.
A submission should take the form of an extended abstract (3 pages long) in PDF format using the NeurIPS 2019 style. Author names do not need to be anonymized, and conflicts of interest in assessing submitted contributions will be based on this (reviewers will not be involved in the assessment of a submission by authors within the same institution). References may extend as far as needed beyond the 3 page upper limit. Submissions may extend beyond the 3 pages upper limit, but reviewers are not expected to read beyond the first 3 pages. If the research has previously appeared in a journal, workshop, or conference (including the NeurIPS 2019 conference), the workshop submission should extend that previous work. Dual submissions to ICLR 2019 and AISTATS 2019 are permitted.
Submissions will be accepted as contributed talks or poster presentations. Extended abstracts should be submitted by September 9, 2019; submission page is here. Final versions will be posted on the workshop website (and are archival but do not constitute a proceedings). Notification of acceptance will be October 1, 2019.
- Extended abstract submission deadline: September 9, 2019 (midnight AOE) (submission page is here)
- Acceptance notification: October 1, 2019
- Camera ready submission: 30 October 2019
- Workshop: December 13 or 14, 2019
We will do our best to guarantee workshop registration for all accepted workshop submissions