Affective computing, a subcategory of artificial intelligence, detects, processes, interprets,
and mimics human emotions. Thanks to the continued advancement of portable non …

Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

Artificial intelligence (AI) researchers have been developing and refining large language
models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks …

Large-scale task planning is a major challenge. Recent work exploits large language
models (LLMs) directly as a policy and shows surprisingly interesting results. This paper …

Supervised learning aims to train a classifier under the assumption that training and test
data are from the same distribution. To ease the above assumption, researchers have …

The correct prediction of heart disease can prevent life threats, and incorrect prediction can
prove to be fatal at the same time. In this paper different machine learning algorithms and …

Many clinicians remain wary of machine learning because of longstanding concerns about
“black box” models.“Black box” is shorthand for models that are sufficiently complex that they …

Perhaps one of the most common and comprehensive statistical and machine learning
algorithms are linear regression. Linear regression is used to find a linear relationship …

There is mounting evidence of emergent phenomena in the capabilities of deep learning
methods as we scale up datasets, model sizes, and training times. While there are some …

We introduce BitFit, a sparse-finetuning method where only the bias-terms of the model (or a
subset of them) are being modified. We show that with small-to-medium training data …