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CRISP achieves up to 38.9% improvement in segmentation accuracy under domain shifts without the need for target-domain data or model updates.
You can slash LLM prompt evaluation costs by 35-60% without sacrificing accuracy by intelligently selecting which examples to use.
Decomposing prompts into independently optimizable "factors" lets you zero in on failure points and slash prompt optimization costs by up to 87%.
Medical image segmentation can be made dramatically more robust to domain shift by focusing on the *rank* of voxel probabilities rather than the probabilities themselves.
Achieve state-of-the-art OCT retinal layer segmentation by reliably aligning data across varying annotation granularities, even with limited labeled data.
Reconstructing horses in 4D from video is now faster and more accurate thanks to a new method that separates motion and appearance, and learns from synthetic data.
