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Bayesian Flow Networks in Continual Learning

arXiv 2310.12001 ↗First posted 18 Oct 2023cs.LG cs.CV stat.MLcited by 0

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Abstract (arXiv)

Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. Their power comes from the expressiveness of neural networks and Bayesian inference which make them suitable in the context of continual learning. We delve into the mechanics behind BFNs and conduct the experiments to empirically verify the generative capabilities on non-stationary data.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 20 references that are in the map. First posted on arXiv 18 Oct 2023.

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References · 17

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5 more
  1. 2021A Variational Perspective on Diffusion-Based Generative Models and Score Matchingcited by 183
  2. 2016Towards Bayesian Deep Learning: A Framework and Some Existing Methodscited by 58
  3. 2018Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologiescited by 43
  4. 2021Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learningcited by 21
  5. 2019BooVAE: Boosting Approach for Continual Learning of VAEcited by 18

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