The study addresses heterogeneous UAV cooperative task assignment under complex constraints via an energy learning ...
Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...
Deep learning has emerged as a transformative tool for the automated detection and classification of seizure events from intracranial EEG (iEEG) recordings. In this review, we synthesize recent ...
Learn the concept of in-context learning and why it’s a breakthrough for large language models. Clear and beginner-friendly explanation. #InContextLearning #DeepLearning #LLMs Supreme Court Deals ...
Deep learning’s (DL’s) promise and appeal is algorithmic amalgamation of all available data to achieve model generalization and prediction of complex systems. Thus, there is a need to design ...
Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. A few public databases provide biological activity data for ...
Ms. Anderson and Ms. Winthrop are the authors of “The Disengaged Teen: Helping Kids Learn Better, Feel Better, and Live Better.” As the new school year gets underway, artificial intelligence is ...
ABSTRACT: Accurately predicting medication response and disease severity is essential for advancing personalized treatment strategies, especially in complex neuropsychiatric conditions. In this study, ...
Abstract: The high cases of Parkinson’s disease (PD) in the senior generation require reliable PD progression prediction. PD can be associated with depression as a common non-motor symptom. Depression ...
Introduction: Seismic first break (FB) picking helps us with near surface tomography, microseismic detection among other tasks. Using image semantic segmentation (ISS) networks to do so has been a hot ...
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