Empirical scaling laws for LLM merging show a size-dependent floor and 1/k-like tail in cross-entropy loss that holds across architectures and merging methods.
Scaling laws for deep learning based image reconstruction
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
An ensemble of stage-specialized text-to-image diffusion models improves prompt alignment over single shared-parameter models while preserving visual quality and inference speed.
Neural scaling laws fitted to subset performance on CAMUS and CEUS echocardiography datasets enable selection of smaller networks achieving state-of-the-art myocardial segmentation with 240-fold parameter reduction and clinical equivalence to expert cardiologists in perfusion quantification.
citing papers explorer
-
Model Merging Scaling Laws in Large Language Models
Empirical scaling laws for LLM merging show a size-dependent floor and 1/k-like tail in cross-entropy loss that holds across architectures and merging methods.
-
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
An ensemble of stage-specialized text-to-image diffusion models improves prompt alignment over single shared-parameter models while preserving visual quality and inference speed.
-
Compute-Optimal Network Design for Echocardiography Myocardial Segmentation and Perfusion Quantification using Neural Scaling Laws
Neural scaling laws fitted to subset performance on CAMUS and CEUS echocardiography datasets enable selection of smaller networks achieving state-of-the-art myocardial segmentation with 240-fold parameter reduction and clinical equivalence to expert cardiologists in perfusion quantification.