Bayesian inference in the space of functions has been an important topic for Bayesian modeling in the past. In this paper, we propose a new solution to this problem called Functional Variational Inference (FVI). In FVI, we minimize a divergence in function space between the variational distribution and the posterior process.

However,theygenerally lose performance inmore realistic scenarios like learning in a continual manner. In contrast, humans can incorporate their prior knowledge to learn new concepts efficiently without forgetting older ones. In this work, we leverage meta-learning to encourage the model to learn how to learn continually. Inspired by human concept learning, we develop agenerative classifier that efficiently uses data-drivenexperience tolearn newconcepts even from fewsamples while being immune to forgetting. Along with cognitiveand theoretical insights, extensiveexperiments onstandard benchmarks demonstrate the effectiveness of the proposed method.

Quantifying uncertainty is important for actionable predictions in real-world applications.

Empowered by neural networks, deep reinforcement learning (DRL) achieves tremendous empirical success. However, DRL requires a large dataset by interacting with the environment, which is unrealistic in critical scenarios such as autonomous driving and personalized medicine. In this paper, we study how to incorporate the dataset collected in the offline setting to improve the sample efficiency in the online setting. To incorporate the observational data, we face two challenges.

As a result, the reverse process gradually denoises and upsamples the latent variable to produce a sample from the data distribution.