Classical randomness has emerged as an important tool to address the challenge of designing quantum algorithms and protocols. Current methods ...
ai has seen significant advancement in coding, math, and reasoning tasks. These advances are largely due to the increased use ...
End-to-end (E2E) neural networks have emerged as flexible and accurate models for multilingual automatic speech recognition (ASR). However, as the ...
Retrieval-augmented generation (RAG), a technique that improves the efficiency of large language models (LLMs) in handling large amounts of text, ...
Whether they are ready or not, across the country, a new generation of kindergartners has entered the school system from ...
One of the main challenges of semilocal density functional theory (DFT) is the consistent underestimation of band gaps, mainly due ...
Generative ai, an area of artificial intelligence, focuses on creating systems capable of producing human-like text and solving complex reasoning ...
Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks. However, they face a major challenge: ...
The application of machine learning to complex decision-making problems, particularly in situations with limited resources and uncertain outcomes, has recently ...
Alignment of language models is very important, particularly in a subset of RLHF methods that have been applied to strengthen ...
