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dchiji-ntt avatar

dchiji-ntt/iterand

0
View on GitHub↗
arxiv.org/abs/2106.09269↗

Iterand

by Daiki Chijiwa*, Shin’ya Yamaguchi, Yasutoshi Ida, Kenji Umakoshi, Tomohiro Inoue

Features

  • Weight Pruning - Iterative randomization for pruning randomly initialized networks.

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10 Stars·2 Forks·Python·4 Aufrufe

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Häufig gestellte Fragen

Was macht dchiji-ntt/iterand?

by Daiki Chijiwa\*, Shin’ya Yamaguchi, Yasutoshi Ida, Kenji Umakoshi, Tomohiro Inoue

Was sind die Hauptfunktionen von dchiji-ntt/iterand?

Die Hauptfunktionen von dchiji-ntt/iterand sind: Weight Pruning.

Welche Open-Source-Alternativen gibt es zu dchiji-ntt/iterand?

Open-Source-Alternativen zu dchiji-ntt/iterand sind unter anderem: tencent/pocketflow — PocketFlow is an integrated toolkit for deep learning model compression, distributed training, and mobile format… chrundle/biprop — This method identifies a binary weight or binary weight and activation subnetwork within a randomly initialized… dem123456789/pruning-deep-neural-networks-from-a-sparsity-perspective — [ICLR 2023] Pruning Deep Neural Networks from a Sparsity Perspective. densoitlab/bitprune — This is the official repo for ICLR 2023 Paper "Bit-Pruning: A Sparse Multiplication-Less Dot-Product" Yusuke Sekikawa… dingxiaoh/gsm-sgd — This repository contains the codes for the following NeurIPS-2019 paper. boone891214/sanity-check-lth — Sample code use for NeurIPS 2021 paper: Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the…

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  • tencent/pocketflowAvatar von Tencent

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    PocketFlow is an integrated toolkit for deep learning model compression, distributed training, and mobile format optimization. It provides a system for reducing the size and complexity of neural networks to improve inference efficiency, featuring a dedicated engine for knowledge distillation and a mobile model optimizer. The framework differentiates itself through an automated hyperparameter tuning system that uses reinforcement learning and statistical models to determine optimal compression ratios and layer-wise bit allocation. It also includes a distributed training system that utilizes mu

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  • chrundle/bipropAvatar von chrundle

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  • dem123456789/pruning-deep-neural-networks-from-a-sparsity-perspectiveAvatar von dem123456789

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  • boone891214/sanity-check-lthAvatar von boone891214

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