Med-Gemini sets new records on 10 of 14 medical benchmarks including 91.1% on MedQA-USMLE, beats GPT-4V by 44.5% on multimodal tasks, and surpasses humans on medical text summarization.
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HiPreNets progressively refines neural networks via residual learning and adaptive techniques to reduce both RMSE and L^∞ errors, outperforming standard networks on Feynman benchmarks and enabling fast high-dimensional ODE surrogates.
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Capabilities of Gemini Models in Medicine
Med-Gemini sets new records on 10 of 14 medical benchmarks including 91.1% on MedQA-USMLE, beats GPT-4V by 44.5% on multimodal tasks, and surpasses humans on medical text summarization.
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HiPreNets: High-Precision Neural Networks through Progressive Training
HiPreNets progressively refines neural networks via residual learning and adaptive techniques to reduce both RMSE and L^∞ errors, outperforming standard networks on Feynman benchmarks and enabling fast high-dimensional ODE surrogates.