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University of Southern California Los Angeles
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3D-PLOT-LLM achieves part-level reasoning in 3D MLLMs without the hefty parameter costs of traditional methods, outperforming competitors on key benchmarks.
Forget fancy transformers: simple summary statistics on irregular time series data can beat SOTA deep learning models in biomedical classification tasks, especially when missing data patterns are predictive.
Achieve state-of-the-art LDCT image restoration with a Green Learning approach that's mathematically transparent, computationally efficient, and memory-friendly.
