RPSFT improves the in-domain versus out-of-domain performance trade-off during LLM supervised fine-tuning by penalizing rotations in pretrained singular subspaces as a proxy for loss-sensitive directions.
and Soures, Nicholas and Kudithipudi, Dhireesha , year=
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Reinforcement learning learns state-dependent parametrization components in idealized climate models that outperform static tuning across several testbeds.
TRACED is a biophysical model that quantifies cell size distribution, extracellular diffusivity, tortuosity, and cell density in human gliomas from time-dependent diffusion MRI data.
citing papers explorer
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Rotation-Preserving Supervised Fine-Tuning
RPSFT improves the in-domain versus out-of-domain performance trade-off during LLM supervised fine-tuning by penalizing rotations in pretrained singular subspaces as a proxy for loss-sensitive directions.
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Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning
Reinforcement learning learns state-dependent parametrization components in idealized climate models that outperform static tuning across several testbeds.
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TRACED: In vivo imaging of extracellular intrinsic diffusivity, tortuosity, cell size distribution and cell density in human glioma patients
TRACED is a biophysical model that quantifies cell size distribution, extracellular diffusivity, tortuosity, and cell density in human gliomas from time-dependent diffusion MRI data.