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Mistaken correlations: Why it's critical to move beyond overly aggregated machine-learning metrics
MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to ...
Abhijeet Sudhakar develops efficient Mamba model training for machine learning, improving sequence modelling and ...
The development of humans and other animals unfolds gradually over time, with cells taking on specific roles and functions ...
More than a decade ago, researchers launched the BabySeq Project, a pilot program to return newborn genomic sequencing results to parents and measure the effects on newborn care. Today, over 30 ...
Depression is one of the most widespread mental health disorders worldwide, affecting approximately 4% of the global ...
A collaborative approach to training AI models can yield better results, but it requires finding partners with data that ...
Machine learning can help predict whether people newly diagnosed with MS will experience disability worsening that occurs ...
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