Within the domain of Natural Language Processing (NLP), Large Language Models (LLMs)
represent sophisticated models engineered to comprehend, generate, and manipulate text …

The scaling of Transformers has driven breakthrough capabilities for language models. At
present, the largest large language models (LLMs) contain upwards of 100B parameters …

Large language models (LLMs) have demonstrated impressive capabilities, but the bar for
clinical applications is high. Attempts to assess the clinical knowledge of models typically …

Large language models (LLMs) have demonstrated impressive capabilities in natural
language understanding and generation, but the quality bar for medical and clinical …

We study how in-context learning (ICL) in language models is affected by semantic priors
versus input-label mappings. We investigate two setups-ICL with flipped labels and ICL with …

Machine learning algorithms typically assume independent and identically distributed
samples in training and at test time (IID). Much work has shown that high-performing ML …

Contemporary pose estimation methods enable precise measurements of behavior via
supervised deep learning with hand-labeled video frames. Although effective in many cases …

Large-scale vision-language models (VLMs) like CLIP successfully find correspondences
between images and text. Through the standard deterministic mapping process, an image or …

Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes
with more evidence. While existing work focuses on using the variance of the Bayesian …

Ensembling neural networks is an effective way to increase accuracy, and can often match
the performance of individual larger models. This observation poses a natural question …