Despite their prevalence in deep-learning communities, over-parameterized models convey high demands of computational costs for proper training.

QuACK's remarkable ability to accelerate gradient-based optimization across a

This simple generative task requires a model to copy binary symbols over long-range dependencies, ignoring the tokens in between. We find that Transformer FFLMs suffer from a long tail of sporadic reasoning errors, some of which we can eliminate using various regularization techniques.

Continual learning entails learning a sequence of tasks and balancing their knowledge appropriately. With limited access to old training samples, much of the current work in deep neural networks has focused on overcoming catastrophic forgetting of old tasks in gradient-based optimization.

We utilize the same experimental setup as the "High-quality Fifty-participant Human

Our novelties lie in three aspects.