Sentence embeddings reduce reconstruction error by 81% in Darcy-flow inversion by providing categorical geological constraints, with limited added value from within-class text detail.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Profiling of Med-DDPM shows cuDNN kernels dominate training; TF32 Tensor Core activation and 3D channels-last layout reduce SM cycles up to 100x and raise Tensor Core utilization on A100 without quality loss.
citing papers explorer
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What Do Language Priors Contribute to Darcy-Flow Inversion? A Mechanistic Audit
Sentence embeddings reduce reconstruction error by 81% in Darcy-flow inversion by providing categorical geological constraints, with limited added value from within-class text detail.
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Performance Analysis and Optimization of 3D Generative Diffusion Models across GPU Architectures
Profiling of Med-DDPM shows cuDNN kernels dominate training; TF32 Tensor Core activation and 3D channels-last layout reduce SM cycles up to 100x and raise Tensor Core utilization on A100 without quality loss.