… robust meta-learning. Motivated by the above arguments, in this paper we propose a Bayesian
meta-learning method, called Bayesian MAML. By introducing Bayesian methods for fast …

… robust meta-learning. Motivated by the above arguments, in this paper we propose a Bayesian
meta-learning method, called Bayesian MAML. By introducing Bayesian methods for fast …

… In this paper, we propose a probabilistic meta-learning … Our approach extends model-agnostic
meta-learning, which adapts to … to a Bayesian version of model-agnostic meta-learning [9]. …

… Meta learning aims at learning a model that can quickly adapt to unseen tasks. Widely …
meta learning methods include modelagnostic meta learning (MAML), implicit MAML, Bayesian …

… We introduce a new, rigorously-formulated Bayesian meta-learning algorithm that learns
a … We show that the models trained with our proposed meta-learning algorithm are well …

… model-agnostic meta-learning baselines: • MAML [5] represents the family of model-agnostic
meta-… Note that we aim to develop a general model-agnostic meta-learning framework and …

Abstract Model-agnostic meta-learning (MAML) formulates meta-learning as a bilevel
optimization problem, where the inner level solves each subtask based on a shared prior, while …

… for gradient-based meta-learning strategies depend on the … To address these issues, we
propose a novel meta-learning … meta-learning framework, Model-Agnostic MetaLearning (…

… Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a …
scheme that eliminates the need to tune meta-learning and learning rates. Our results with the …

… Modelagnostic meta-learning for fast adaptation of deep networks. In International …
Probabilistic model-agnostic meta-learning. Advances in neural information processing systems, 31, …