Results 261 to 270 of about 114,287 (303)
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Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes
arXiv.orgStructured pruning is a promising approach to create smaller, faster large language models. However, existing methods typically rely on computing the gradient via backward passes, which can inflate memory requirements and compute costs.
L. Dery +5 more
semanticscholar +1 more source
LAPTOP-Diff: Layer Pruning and Normalized Distillation for Compressing Diffusion Models
arXiv.orgIn the era of AIGC, the demand for low-budget or even on-device applications of diffusion models emerged. In terms of compressing the Stable Diffusion models (SDMs), several approaches have been proposed, and most of them leveraged the handcrafted layer ...
Dingkun Zhang +4 more
semanticscholar +1 more source
SlimGPT: Layer-wise Structured Pruning for Large Language Models
Neural Information Processing SystemsLarge language models (LLMs) have garnered significant attention for their remarkable capabilities across various domains, whose vast parameter scales present challenges for practical deployment. Structured pruning is an effective method to balance model
Gui Ling +3 more
semanticscholar +1 more source
A guide to successful pruning. Pruning basics and tools
2014Discusses reasons for pruning and various pruning tools.
French, Sue (Sue C.) +1 more
openaire +2 more sources
A guide to successful pruning. Pruning deciduous trees
2014Describes proper pruning techniques for deciduous trees.
Appleton, Bonnie L. +1 more
openaire +2 more sources
Synaptic Pruning by Microglia Is Necessary for Normal Brain Development
Science, 2011R. Paolicelli +11 more
semanticscholar +1 more source
Channel pruning based on convolutional neural network sensitivity
Neurocomputing, 2022Chenbin Yang, Huiyi Liu
exaly