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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 ...
From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...
Background While the incidence of hospital adverse events appeared to be declining before 2019, the COVID-19 pandemic may ...
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