Meta-learning empowers learning systems with the ability to acquire knowledge from
multiple tasks, enabling faster adaptation and generalization to new tasks. This review …

Artificial neural networks (ANNs) have emerged as an essential tool in machine learning,
achieving remarkable success across diverse domains, including image and speech …

At present, the mechanisms of in-context learning in Transformers are not well understood
and remain mostly an intuition. In this paper, we suggest that training Transformers on auto …

Large pretrained language models have shown surprising in-context learning (ICL) ability.
With a few demonstration input-label pairs, they can predict the label for an unseen input …

Neural sequence models based on the transformer architecture have demonstrated
remarkable\emph {in-context learning}(ICL) abilities, where they can perform new tasks …

In-context learning (ICL) is a type of prompting where a transformer model operates on a
sequence of (input, output) examples and performs inference on-the-fly. In this work, we …

We observe that pre-trained large language models (LLMs) are capable of autoregressively
completing complex token sequences--from arbitrary ones procedurally generated by …

Large transformer models trained on diverse datasets have shown a remarkable ability to
learn in-context, achieving high few-shot performance on tasks they were not explicitly …

Structured state space sequence (S4) models have recently achieved state-of-the-art
performance on long-range sequence modeling tasks. These models also have fast …

Multivariate time series forecasting focuses on predicting future values based on historical
context. State-of-the-art sequence-to-sequence models rely on neural attention between …