Найденные страницы с тегом pruning всего 695

Comprehensive bench-marking of entropy and margin based scoring metrics for data selection - Amazon Science

While data selection methods have been studied extensively in active learning, data pruning, and data augmentation settings, there is little evidence for the efficacy of these methods in industry scale settings, particularly in low-resource languages. Our work presents ways of assessing prospective…

NNCF Explained | Papers With Code

Neural Network Compression Framework, or NNCF, is a Python-based framework for neural network compression with fine-tuning. It leverages recent advances of various network compression methods and implements some of them, namely quantization, sparsity, filter pruning and binarization. These methods allow producing more hardware-friendly models that can be efficiently run on general-purpose hardware computation units (CPU, GPU) or specialized deep learning accelerators.

What is AI Pruning? Definition from Techopedia.com

AI pruning is a collection of strategies for editing a neural network to make it as lean as possible without impacting output accuracy.

Google warns against content pruning as CNET deletes thousands of pages

Google says deleting old content just because it's old isn't beneficial for SEO. However, pruning content can be helpful – when done right.

Structural pruning of large language models via neural architecture search - Amazon Science

Large Language Models (LLM) achieved considerable results on natural language understanding tasks. However, their sheer size causes a large memory consumption or high latency at inference time, which renders deployment on hardware-constrained applications challenging. Neural architecture search…

Редукция нейронных сетей при помощи вариационной оптимизации / Хабр

Привет, Хабр. Сегодня я бы хотел развить тему вариационной оптимизации и рассказать, как применить её к задаче обрезки малоинформативных каналов в нейронных сетях (pruning). При помощи неё можно...

Прунинг нейронных сетей (фитнес бывает полезен не только людям) / Хабр

Всем привет! В данном посте я хотел бы рассказать про весьма интересную и важную деятельность в области глубокого обучения как прореживание (прунинг) нейронных сетей. На просторах сети есть неплохие...

Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression | Papers With Code

2 code implementations in PyTorch. In this paper, we analyze two popular network compression techniques, i.e. filter pruning and low-rank decomposition, in a unified sense. By simply changing the way the sparsity regularization is enforced, filter pruning and low-rank decomposition can be derived accordingly. This provides another flexible choice for network compression because the techniques complement each other. For example, in popular network architectures with shortcut connections (e.g. ResNet), filte

DHP: Differentiable Meta Pruning via HyperNetworks | Papers With Code

2 code implementations in PyTorch. Network pruning has been the driving force for the acceleration of neural networks and the alleviation of model storage/transmission burden. With the advent of AutoML and neural architecture search (NAS), pruning has become topical with automatic mechanism and searching based architecture optimization. Yet, current automatic designs rely on either reinforcement learning or evolutionary algorithm. Due to the non-differentiability of those algorithms, the pruning algorithm