In-context learning is the ability of a model to condition on a prompt sequence consisting of
in-context examples (input-output pairs corresponding to some task) along with a new query …

Abstract Machine learning has transformed many fields and has recently found applications
in chemistry and materials science. The small datasets commonly found in chemistry …

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 …

This paper presents a new perspective on time series forecasting. In existing time series
forecasting methods, the models take a sequence of numerical values as input and yield …

A central notion in practical and theoretical machine learning is that of a weak learner,
classifiers that achieve better-than-random performance (on any given distribution over …

Large Language Models (LLMs) have seen significant use in domains such as natural
language processing and computer vision. Going beyond text, image and graphics, LLMs …

Contrastive representation learning is crucial in medical time series analysis as it alleviates
dependency on labor-intensive, domain-specific, and scarce expert annotations. However …

Foundation large language models (LLMs) have shown an impressive ability to solve tasks
across a wide range of fields including health. To effectively solve personalized health tasks …

Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method that leverages low-
rank adaptation of weight matrices, has emerged as a prevalent technique for fine-tuning pre …