Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm
This addresses the foundational problem of universality in meta-learning for AI researchers, providing a theoretical proof and empirical validation.
The paper tackles the problem of whether deep representations combined with gradient descent can approximate any learning algorithm in meta-learning, and finds that this is true, with experiments showing gradient-based methods generalize more widely than recurrent models.
Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently. A popular approach to meta-learning is to train a recurrent model to read in a training dataset as input and output the parameters of a learned model, or output predictions for new test inputs. Alternatively, a more recent approach to meta-learning aims to acquire deep representations that can be effectively fine-tuned, via standard gradient descent, to new tasks. In this paper, we consider the meta-learning problem from the perspective of universality, formalizing the notion of learning algorithm approximation and comparing the expressive power of the aforementioned recurrent models to the more recent approaches that embed gradient descent into the meta-learner. In particular, we seek to answer the following question: does deep representation combined with standard gradient descent have sufficient capacity to approximate any learning algorithm? We find that this is indeed true, and further find, in our experiments, that gradient-based meta-learning consistently leads to learning strategies that generalize more widely compared to those represented by recurrent models.