TabSCM produces causally consistent tabular data by orienting a CPDAG into a DAG, fitting root marginals with KDE, and using conditional diffusion plus trees for child nodes, outperforming GANs and diffusion baselines on fidelity, utility, and privacy across seven datasets.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Introduces Hybrid Tuning adapter with frequency filtering and noise estimation to adapt CLIP for ultrasound segmentation and classification, claiming outperformance on six multi-center datasets.
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TabSCM: A practical Framework for Generating Realistic Tabular Data
TabSCM produces causally consistent tabular data by orienting a CPDAG into a DAG, fitting root marginals with KDE, and using conditional diffusion plus trees for child nodes, outperforming GANs and diffusion baselines on fidelity, utility, and privacy across seven datasets.
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Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis
Introduces Hybrid Tuning adapter with frequency filtering and noise estimation to adapt CLIP for ultrasound segmentation and classification, claiming outperformance on six multi-center datasets.
- D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models