Researchers have developed a machine learning model capable of predicting whether a patient with depression will respond to ...
A collaborative approach to training AI models can yield better results, but it requires finding partners with data that complements your own.
A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...
Antimicrobial resistance (AMR) is an increasingly dangerous problem affecting global health. In 2019 alone, methicillin-resistant Staphylococcus aureus (MRSA) accounted for more than 100,000 global ...
AI and machine learning are revolutionizing drug discovery, development, and lifecycle management, addressing industry ...
Abhijeet Sudhakar develops efficient Mamba model training for machine learning, improving sequence modelling and ...
When experiments are impractical, density functional theory (DFT) calculations can give researchers accurate approximations of chemical properties. The mathematical equations that underpin the ...
A new technical paper titled “Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate” was published by researchers at KTH Royal Institute of Technology and ...
9don MSN
Machine learning identifies factors that may determine the age of onset of Huntington's disease
A team from the Faculty of Medicine and Health Sciences and the Institute of Neurosciences at the University of Barcelona ...
An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy inefficiency in power amplifiers for next-generation wireless communication.
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