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NeurIPS Europe meetup on Bayesian Deep Learning
December 10, 2020 @ 10:00 am - 7:00 pm UTC+0
FreeThis year the BDL workshop will take a new form, and will be organised as a NeurIPS European event together with the ELLIS programme on Robustness in ML. The event will be virtual, taking place in Gather.Town, with a schedule and socials to accommodate European timezones. Participants are welcome to join from around the world though.
No registration is required for the NeurIPS Europe meetup on Bayesian Deep Learning, and the event will be open to all.
1. Call for postersWe invite researchers to submit posters in any of the following areas:
- Uncertainty in deep learning,
- Applications of Bayesian 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),
- Generative deep models (such as variational autoencoders),
- Information theory in deep learning,
- Deep ensemble uncertainty,
- NTK and Bayesian modelling,
- Connections between NNs and GPs,
- Incorporating explicit prior knowledge in deep learning (such as posterior regularisation with logic rules),
- Approximate inference for Bayesian deep learning (such as variational Bayes / expectation propagation / etc. in Bayesian neural networks),
- Scalable MCMC inference in Bayesian deep models,
- Deep recognition models for variational inference (amortised inference),
- Bayesian deep reinforcement learning,
- Deep learning with small data,
- Deep learning in Bayesian modelling,
- Probabilistic semi-supervised learning techniques,
- Active learning and Bayesian optimisation for experimental design,
- Kernel methods in Bayesian deep learning,
- Implicit inference,
- Applying non-parametric methods, one-shot learning, and Bayesian deep learning in general.
You are welcome to submit research that has previously appeared in a journal, workshop, or conference (including the NeurIPS 2020 conference and AABI), as the poster presentation is intended to be a platform for discussion of research of interest to the BDL community.A submission should take the form of a poster in PDF format (1-page PDF of maximum size 5MB in landscape orientation). Attendees will only have regular computer screens to see it in its entirety, so please do not over-crowd your poster. The title should be on the top of the poster and use large fonts, as this is what will be shown to attendees as they approach your poster, see the screenshot here. Author names do not need to be anonymised during submission. A light-weight editorial review will be carried out, and only posters of no relevance to the community will be rejected.Posters should be submitted by December 1, 2020; please email poster submissions to bayesiandeeplearning2020@gmail.com. Posters will be posted on this website (and are archival but do not constitute a proceedings). Notification of acceptance will be made before December 5, 2020.2. Key Dates
- Poster submission deadline: December 1, 2020 (23:59 AOE) (submit to this email bayesiandeeplearning2020@gmail.com)
- Acceptance notification: before December 5, 2020
- Workshop presentations and talks: Thursday, 10 December, 2020
3. Confirmed Speakers
- Daniela Rus (MIT CSAIL)
- Mark van der Wilk (Imperial College London)
- Mihaela van Der Schaar (University of Cambridge)
- Durk Kingma (Google)
- David Duvenaud (University of Toronto)
- Tal Arbel (MILA)
- Zack Chase Lipton (CMU)