fMLF

fMLF is an acronym that can refer to more than one concept across disciplines. In signal processing, fMLF stands for fast Maximum Likelihood Filter, a class of adaptive denoising filters that approximate the solution of a maximum likelihood problem with reduced computational complexity. By assuming a statistical model for the noisy observations, these filters iteratively estimate the underlying signal using likelihood maximization, trading optimality for speed to enable real-time operation in communications and audio processing.

In machine learning and data science, fMLF can denote a Functional Meta-Learning Framework, a paradigm that treats learning across tasks as estimation in a function space. It aims to enable rapid adaptation to new tasks (few-shot learning) by learning a functional prior or shared representation that parametrizes task-specific models. Proposals under this label explore how to represent tasks functionally and how to optimize across tasks.

Because acronym usage varies, the intended meaning of fMLF should be determined from the context in which it appears. There is no single, universally accepted definition.