BaLoRA is a Bayesian LoRA variant with input-adaptive noise that improves accuracy over standard LoRA and supplies well-calibrated uncertainty estimates on language, vision, and scientific prediction tasks.
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DiMS is a physics-inspired dynamical sampler guaranteed to exactly sample reparameterization-invariant minimum level sets in neural network loss landscapes.
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BaLoRA: Bayesian Low-Rank Adaptation of Large Scale Models
BaLoRA is a Bayesian LoRA variant with input-adaptive noise that improves accuracy over standard LoRA and supplies well-calibrated uncertainty estimates on language, vision, and scientific prediction tasks.
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Don't Stop Me Yet: Sampling Loss Minima via Dissipative Riemannian Mechanics
DiMS is a physics-inspired dynamical sampler guaranteed to exactly sample reparameterization-invariant minimum level sets in neural network loss landscapes.