Meta-learning with memory-augmented neural networks

TLDR

Despite recent breakthroughs in deep neural networks, one‑shot learning remains a persistent challenge because traditional gradient‑based models require large data and must relearn parameters inefficiently when encountering new data, whereas memory‑augmented architectures such as Neural Turing Machines can quickly encode and retrieve new information. The study demonstrates that a memory‑augmented neural network can rapidly assimilate new data and use it to make accurate predictions after only a few samples. The authors employ a memory‑augmented neural network with a novel content‑focused external memory access method, building on Neural Turing Machine architectures to quickly encode and retrieve new information. The memory‑augmented neural network accurately predicts outcomes after only a few samples, demonstrating rapid assimilation of new data.

Abstract

Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of one-shot learning. Traditional gradient-based networks require a lot of data to learn, often through extensive iterative training. When new data is encountered, the models must inefficiently relearn their parameters to adequately incorporate the new information without catastrophic interference. Architectures with augmented memory capacities, such as Neural Turing Machines (NTMs), offer the ability to quickly encode and retrieve new information, and hence can potentially obviate the downsides of conventional models. Here, we demonstrate the ability of a memory-augmented neural network to rapidly assimilate new data, and leverage this data to make accurate predictions after only a few samples. We also introduce a new method for accessing an external memory that focuses on memory content, unlike previous methods that additionally use memory location-based focusing mechanisms.