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BaLoRA introduces a Bayesian extension to Low-Rank Adaptation (LoRA) that provides calibrated uncertainty estimates and improves prediction accuracy by narrowing the gap with full fine-tuning.
DyStruct is a training-free Bayesian decoding framework for discrete Diffusion Language Models that enables flexible-length generation by dynamically determining expansion size and decoding order, improving accuracy on math and code tasks.
This paper presents a Bayesian inverse problem framework for rain field reconstruction using Commercial Microwave Links and Diffusion Model priors, demonstrating improved accuracy over existing baselines.