Probabilistic Model-Agnostic Meta-Learning Reading Notes: Probabilistic Model-Agnostic Meta-Learning This post is a reading note for the paper "Probabilistic Model-Agnostic Meta-Learning" by Finn et al. It is a successive work to the famous MAML paper , and can be viewed as the Bayesian version of the MAML model. Introduction When dealing with different tasks of the same family, for example, the image classification family, the neural language processing family, etc.. It is usually preferred to be able to acquire solutions to complex tasks from only a few samples given the past knowledge of other tasks as a prior (few shot learning). The idea of learning-to-learn, i.e., meta-learning, is such a framework. What is meta-learning? The model-agnostic meta-learning (MAML) [1] is a few shot meta-learning algorithm that uses gradient descent to adapt the model at meta-test time to a new few-shot task, and trains the model parameters at meta-training time to enable rapid adap...