Meta-learning, or learning-to-learn, has proven to be a successful strategy in attacking problems in supervised learning and reinforcement learning that involve small amounts of data. State-of-the-art solutions involve learning an initialization and/or learning algorithm using a set of training episodes so that the meta learner can generalize to an evaluation episode quickly. These methods perform well but often lack good quantification of uncertainty, which can be vital to real-world applications when data is lacking. We propose a meta-learning method which efficiently amortizes hierarchical variational inference across tasks, learning a prior distribution over neural network weights so that a few steps of Bayes by Backprop will produce a good task-specific approximate posterior. We show that our method produces good uncertainty estimates on contextual bandit and few-shot learning benchmarks. Deep learning has achieved success in domains that involve a large amount of labeled data BID26 or training samples BID23 BID30 . However, a key aspect of human intelligence is our ability to learn new concepts from only a few experiences. It has been hypothesized that this skill arises from accumulating prior knowledge and using it appropriately in new settings BID19 .Meta learning attempts to endow machine learning models with the same ability by training a metalearner to perform well on a distribution of training tasks. The meta-learner is then applied to an unseen task, usually assumed to be drawn from a task distribution similar to the one used for training, with the hope that it can learn to solve the new task efficiently. Popular meta-learning methods have advanced the state-of-the-art in many tasks, including the few-shot learning problem, where the model has to learn a new task given a small training set containing as few as one example per class. Though performance on few-shot learning benchmarks has greatly increased in the past few years, it is unclear how well the associated methods would perform in real-world settings, where the relationship between training and evaluation tasks could be tenuous. For success in the wild, in addition to good predictive accuracy, it is also important for meta-learning models to have good predictive uncertainty -to express high confidence when a prediction is likely to be correct but display low confidence when a prediction could be unreliable. This type of guarantee in predictive ability would allow appropriate human intervention when a prediction is known to have high uncertainty.Bayesian methods offer a principled framework to reason about uncertainty, and approximate Bayesian methods have been used to provide deep learning models with accurate predictive uncertainty BID7 BID20 . By inferring a posterior distribution over neural network weights, we can produce a posterior predictive distribution that properly indicates the level of confidence on new unseen examples. Accordingly, we consider meta-learning under a Bayesian view in order to transfer the aforementioned benefits to our setting. Specifically , we extend the work of BID0 , who considered hierarchical variational inference for meta-learning. The work primarily dealt with PAC-Bayes bounds in meta-learning and the experiments consisted of data with tens of training episodes and small networks. In this paper, we show how the meta-learning framework of BID5 can be used to efficiently amortize variational inference for the Bayesian model of BID0 in order to combine the former's flexibility and scalability with the latter's uncertainty quantification. DISPLAYFORM0 We described a method to efficiently use hierarchical variational inference to learn a meta-learning model that is scalable across many training episodes and large networks. The method corresponds to learning a prior distribution over the network weights so that a few steps of Bayes by Backprop will produce a good approximate posterior. Through various experiments we show that using a Bayesian interpretation allows us to reason effectively about uncertainty in contextual bandit and CIFAR-100: 1-shot, 5-class CIFAR-100: 1-shot, 10-class miniImageNet: 1-shot, 5-class few-shot learning tasks. The proposed method is flexible and future work could involve considering more expressive prior (and corresponding posterior) distributions to further improve the uncertainty estimates.
We propose a meta-learning method which efficiently amortizes hierarchical variational inference across training episodes. An adaptation to MAML-type models that accounts for posterior uncertainty in task specific latent variables by employing variational inference for task-specific parameters in a hierarchical Bayesian view of MAML. The authors consider meta-learning to learn a prior over neural network weights, done via amortized variational inference.