Fast Context Adaptation via Meta-Learning

In this article, we propose a new framework called CAMEL (Composite Adaptive Meta-Learning) that simplifies multi-task learning by combining multiple tasks into a single neural network. Unlike traditional approaches that require separate models for each task, CAMEL uses a single model with adaptive weights to learn all tasks simultaneously.
Our key insight is that different tasks often share similar structure and parameters, so we can leverage this shared structure to learn the tasks more efficiently. We achieve this by defining a meta-learning algorithm that learns to adapt the neural network’s weights for each task while ensuring they are close to their linear approximation. This allows us to use simple gradient descent updates to update the weights without overfitting to any individual task.
We demonstrate the effectiveness of CAMEL on several benchmark datasets and compare it to state-of-the-art multi-task learning methods. Our results show that CAMEL achieves better performance than existing approaches while requiring fewer parameters and less computational resources. Additionally, we provide a detailed analysis of how CAMEL’s interpretability can be used to understand the relationships between different tasks and identify new task combinations with promising performance.
Our proposed framework has significant implications for real-world applications where tasks are often diverse and complex. By simplifying the multi-task learning process, CAMEL makes it easier for practitioners to apply multi-task learning to a wide range of domains without requiring extensive expertise in each task. With its efficiency and interpretability, CAMEL is poised to become a go-to method for many applications in machine learning.