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Incorporating perceptual regularization into diffusion models significantly enhances image super-resolution, preserving fine details that traditional methods often overlook.
R-ItCUR achieves accurate tensor recovery even in the presence of significant outliers, showcasing its robustness and efficiency in real-world applications.
Current evaluation practices for agentic AI in medicine are misaligned with clinical needs, risking the reliability of these systems in real-world applications.
Energy-based learning can significantly enhance the robustness and accuracy of tensegrity structure predictions, overcoming traditional method limitations.
FlowPET not only recovers low-contrast lesions better than current methods but does so by leveraging Hamiltonian dynamics to preserve weak signals in high-noise environments.
By alternating between segmentation and curve modeling, A2ONet achieves more accurate liver landmark detection in laparoscopic images, even under poor illumination.
