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dchiji-ntt avatar

dchiji-ntt/iterand

0
View on GitHub↗
10 estrellas·2 forks·Python·5 vistasarxiv.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.

Historial de estrellas

Gráfico del historial de estrellas de dchiji-ntt/iterandGráfico del historial de estrellas de dchiji-ntt/iterand

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Alternativas open-source a Iterand

Proyectos open-source similares, clasificados según cuántas características comparten con Iterand.
  • tencent/pocketflowAvatar de Tencent

    Tencent/PocketFlow

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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 de chrundle

    chrundle/biprop

    51Ver en GitHub↗

    This method identifies a binary weight or binary weight and activation subnetwork within a randomly initialized network that achieves performance comparable to, and sometimes better than, a weight-optimized network. The resulting binarized and pruned networks that achieve comparable performance…

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    Ver en GitHub↗51
  • dem123456789/pruning-deep-neural-networks-from-a-sparsity-perspectiveAvatar de dem123456789

    dem123456789/Pruning-Deep-Neural-Networks-from-a-Sparsity-Perspective

    25Ver en GitHub↗

    ICLR 2023 Pruning Deep Neural Networks from a Sparsity Perspective

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    Ver en GitHub↗25
  • boone891214/sanity-check-lthAvatar de boone891214

    boone891214/sanity-check-LTH

    8Ver en GitHub↗

    Sample code use for NeurIPS 2021 paper: Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?

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Ver las 30 alternativas a Iterand→

Preguntas frecuentes

¿Qué hace dchiji-ntt/iterand?

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

¿Cuáles son las características principales de dchiji-ntt/iterand?

Las características principales de dchiji-ntt/iterand son: Weight Pruning.

¿Qué alternativas de código abierto existen para dchiji-ntt/iterand?

Las alternativas de código abierto para dchiji-ntt/iterand incluyen: 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…