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École polytechnique, Institut Polytechnique de Paris
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Medical records and cardiac biomarkers alone outperform vascular graphs in predicting pulmonary embolism risk, raising questions about the value of complex vascular representations.
Evolutionary algorithms augmented with local search (Baldwinian and Lamarckian evolution) can handily beat deep learning baselines on graph problems, suggesting a resurgence for these classic techniques.
Text-to-image models get a diversity boost without sacrificing quality, thanks to a new sampling method that disentangles and controls prompt-dependent and independent variations in the CLIP embedding space.
