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Nexitally不能用了-outline

Slides for invited talks are now online (see 手机ip地址更换 below), as well as workshop video recordings.

Nexitally不能用了-outline

Nexitally不能用了-outline

Nexitally不能用了-outline

We added all camera ready submissions sent to us by 11/12/2024. If a paper is not online, please contact the lead author and encourage them to send us the camera ready.

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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),
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• practical approximate inference techniques in Bayesian deep learning,
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• applications of Bayesian deep learning,
• or any of the topics 如何更换手机ip.

A submission should take the form of an extended abstract (3 pages long) in PDF format using the NeurIPS 2024 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 2024 conference), the workshop submission should extend that previous work. Dual submissions to ICLR 2024, AAAI 2024, and AISTATS 2024 are permitted.

Submissions will be accepted as contributed talks or poster presentations. Extended abstracts should be submitted by September 9, 2024 Deadline has been extended to Friday, September 13, 2024; submission page is 手机ip地址更换国外. Final versions will be posted on the workshop website (and are archival but do not constitute a proceedings). Notification of acceptance will be made before October 1, 2024.

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• Extended abstract submission deadline: September 9, 2024 (23:59 AOE) Deadline has been extended to Friday, September 13, 2024 (23:59 AOE) (submission page is here)
• Acceptance notification: before October 1, 2024
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• Workshop: Friday, December 13, 2024

Please make sure to apply to the lottery registration as the workshop is allocated a limited number of tickets. We will do our best to guarantee workshop registration for all accepted workshop submissions which did not receive a lottery registration.

Nexitally不能用了-outline

While deep learning has been revolutionary for machine learning, most modern deep learning models cannot represent their uncertainty nor take advantage of the well studied tools of probability theory. This has started to change following recent developments of tools and techniques combining Bayesian approaches with deep learning. The intersection of the two fields has received great interest from the community over the past few years, with the introduction of new deep learning models that take advantage of Bayesian techniques, as well as Bayesian models that incorporate deep learning elements [1-11]. In fact, the use of Bayesian techniques in deep learning can be traced back to the 1990s’, in seminal works by Radford Neal [12], David MacKay [13], and Dayan et al. [14]. These gave us tools to reason about deep models’ confidence, and achieved state-of-the-art performance on many tasks. However earlier tools did not adapt when new needs arose (such as scalability to big data), and were consequently forgotten. Such ideas are now being revisited in light of new advances in the field, yielding many exciting new results.

Extending on last year’s workshop’s success, this workshop will again study the advantages and disadvantages of such ideas, and will be a platform to host the recent flourish of ideas using Bayesian approaches in deep learning and using deep learning tools in Bayesian modelling. The program includes a mix of invited talks, contributed talks, and contributed posters. It will be composed of five themes: deep generative models, variational inference using neural network recognition models, practical approximate inference techniques in Bayesian neural networks, applications of Bayesian neural networks, and information theory in deep learning. Future directions for the field will be debated in a panel discussion.

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Our 2018 workshop page is available here; Our 2017 workshop page is available here; Our 2016 workshop page is available here; videos from the 2016 workshop are available online as well.

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1. Kingma, DP and Welling, M, ‘’Auto-encoding variational bayes’’, 2013.
2. Rezende, D, Mohamed, S, and Wierstra, D, ‘’Stochastic backpropagation and approximate inference in deep generative models’’, 2014.
3. Blundell, C, Cornebise, J, Kavukcuoglu, K, and Wierstra, D, ‘’Weight uncertainty in neural network’’, 2015.
4. Hernandez-Lobato, JM and Adams, R, ’’Probabilistic backpropagation for scalable learning of Bayesian neural networks’’, 2015.
5. 更换IP-云东方:标签：更换IP iPhone 手机网络怎么快速更换IP地址,IPhone6怎么刷ip地址 1 在网上抢手机？刷邀请码？抢内测资格这些七七八八的活动，它伔都有对IP址的限制基本上是一个IP限制一次机会。
6. Gal, Y and Ghahramani, G, ‘’Bayesian convolutional neural networks with Bernoulli approximate variational inference’’, 2015.
7. Kingma, D, Salimans, T, and Welling, M. ‘’Variational dropout and the local reparameterization trick’’, 2015.
8. Balan, AK, Rathod, V, Murphy, KP, and Welling, M, ‘’Bayesian dark knowledge’’, 2015.
9. Louizos, C and Welling, M, “Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors”, 2016.
10. Lawrence, ND and Quinonero-Candela, J, “Local distance preservation in the GP-LVM through back constraints”, 2006.
11. Tran, D, Ranganath, R, and Blei, DM, “Variational Gaussian Process”, 2015.
12. Neal, R, ‘’Bayesian Learning for Neural Networks’’, 1996.
13. MacKay, D, ‘’A practical Bayesian framework for backpropagation networks‘’, 1992.
14. Dayan, P, Hinton, G, Neal, R, and Zemel, S, ‘’The Helmholtz machine’’, 1995.
15. Wilson, AG, Hu, Z, Salakhutdinov, R, and Xing, EP, “Deep Kernel Learning”, 2016.
16. Saatchi, Y and Wilson, AG, “Bayesian GAN”, 2017.
17. MacKay, D.J.C. “Bayesian Methods for Adaptive Models”, PhD thesis, 1992.