Web Reference: [1] Papamakarios, George, et al. "Normalizing flows for probabilistic modeling and inference." Journal of Machine Learning Research 22.57 (2021): 1-64. [2] Kobyzev, Ivan, Simon JD Prince, and Marcus A. Brubaker. "Normalizing flows: An introduction and review of current methods." herent and comprehensive review of the literature around the construction and use of Normalizing Flows for distribution learning. We aim to provide context and e. planation of the models, review current state-of-the-art literature, and identify open questions and promising future dire. In this review, we attempt to provide such a perspective by describing flows through the lens of probabilistic modeling and inference. We place special emphasis on the fundamental principles of flow design, and discuss foundational topics such as expressive power and computational trade-offs.
YouTube Excerpt: Uros Seljak, UC Berkeley.
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