Delve into the cutting edge of Artificial Intelligence with our dedicated Research Paper section.
Asymptotics of Network Embeddings Learned via Subsampling Andrew Davison, Morgane Austern; 24(138):1−120, 2023. Abstract Network data are ubiquitous in modern machine learning, with tasks of interest including node classification, node clustering and link prediction. A frequent approach begins by learning…
![Generalist embedding models are better at short-context clinical semantic search than specialized embedding models. (arXiv:2401.01943v1 [cs.CL])](http://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png "Generalist embedding models are better at short-context clinical semantic search than specialized embedding models. (arXiv:2401.01943v1 [cs.CL])"){kind=logo}
The increasing use of tools and solutions based on Large Language Models (LLMs) for various tasks in the medical domain has become a prominent trend. Their use in this highly critical and sensitive domain has thus raised important questions about…
Metaverse is trending to create a digital circumstance that can transfer the real world to an online platform supported by large quantities of real-time interactions. Pre-trained Artificial Intelligence (AI) models are demonstrating their increasing capability in aiding the metaverse to…
In recent years, foundation models (FMs) have solidified their role as cornerstone advancements in the deep learning domain. By extracting intricate patterns from vast datasets, these models consistently achieve state-of-the-art results across a spectrum of downstream tasks, all without necessitating…
[Submitted on 3 Jan 2024] Authors:Ernest Perkowski, Rui Pan, Tuan Dung Nguyen, Yuan-Sen Ting, Sandor Kruk, Tong Zhang, Charlie O’Neill, Maja Jablonska, Michael J. Smith, Kevin Schawinski, Kartheik Iyer, Ioana Ciucă for UniverseTBD Download a PDF of the paper titled…