Glow: Generative Flow with Invertible 1x1 Convolutions, Kingma and Dhariwal, 2018, arXiv Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design, Jonathan Ho et al.

Variational Inference with Normalizing Flows, Rezende and Mohamed, 2015, arXiv Density Estimation using Real NVP, Dinh et al.

Hyunjik kim et al., 2019, arXiv Keyword: Bayesian, Process Problem: Underfitting of Neural Process Solution: NP + Self-Attention, Cross-Attention Benefits: Improvement of prediction accuracy, training speed, model capability.

Marta Garnelo et al., 2018, arXiv Keyword: Bayesian, Process Problem: Data inefficiency, hard to train multiple datasets in one.

Marta Garnelo et al., 2018, arXiv Keyword: Bayesian, Process Problem: Weakness of knowledge sharing and data inefficiency of classical supervised learning Solution: Stochastic Process + NN Benefits: Data efficient, prior sharing Contribution: Encapsulation of parameterized NN function family.