awesome-repositories.comKategorienBlog
MCP
awesome-repositories.com

Entdecke die besten Open-Source-Repositories mit KI-gestützter Suche.

EntdeckenKuratierte SuchenOpen-Source-AlternativenSelf-hosted SoftwareBlogSitemap
ProjektMCP-ServerÜber unsRanking-MethodikPresse
RechtlichesDatenschutzAGB
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·
ganguli-lab avatar

ganguli-lab/degrees-of-freedom

0
View on GitHub↗
37 Stars·4 Forks·Python·5 Aufrufe

Degrees Of Freedom

This repository contains source code for the ICLR 2022 paper How many degrees of freedom do we need to train deep networks: a loss landscape perspective by Brett W. Larsen, Sanislav Fort, Nic Becker, and Surya Ganguli (arXiv version).

Features

  • Weight Pruning - Loss landscape perspective on required degrees of freedom.

Star-Verlauf

Star-Verlauf für ganguli-lab/degrees-of-freedomStar-Verlauf für ganguli-lab/degrees-of-freedom

KI-Suche

Entdecke weitere awesome Repositories

Beschreibe in einfachen Worten, was du brauchst — die KI bewertet tausende kuratierte Open-Source-Projekte nach Relevanz.

Start searching with AI

Open-Source-Alternativen zu Degrees Of Freedom

Ähnliche Open-Source-Projekte, sortiert nach der Anzahl der gemeinsamen Funktionen mit Degrees Of Freedom.
  • tencent/pocketflowAvatar von Tencent

    Tencent/PocketFlow

    2,914Auf GitHub ansehen↗

    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

    Pythonautomlcomputer-visiondeep-learning
    Auf GitHub ansehen↗2,914
  • chrundle/bipropAvatar von chrundle

    chrundle/biprop

    51Auf GitHub ansehen↗

    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…

    Python
    Auf GitHub ansehen↗51
  • dchiji-ntt/iterandAvatar von dchiji-ntt

    dchiji-ntt/iterand

    10Auf GitHub ansehen↗

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

    Python
    Auf GitHub ansehen↗10
  • boone891214/sanity-check-lthAvatar von boone891214

    boone891214/sanity-check-LTH

    8Auf GitHub ansehen↗

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

    Python
    Auf GitHub ansehen↗8
Alle 30 Alternativen zu Degrees Of Freedom anzeigen→

Häufig gestellte Fragen

Was macht ganguli-lab/degrees-of-freedom?

This repository contains source code for the ICLR 2022 paper How many degrees of freedom do we need to train deep networks: a loss landscape perspective by Brett W. Larsen, Sanislav Fort, Nic Becker, and Surya Ganguli (arXiv version).

Was sind die Hauptfunktionen von ganguli-lab/degrees-of-freedom?

Die Hauptfunktionen von ganguli-lab/degrees-of-freedom sind: Weight Pruning.

Welche Open-Source-Alternativen gibt es zu ganguli-lab/degrees-of-freedom?

Open-Source-Alternativen zu ganguli-lab/degrees-of-freedom 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… dchiji-ntt/iterand — by Daiki Chijiwa\*, Shin’ya Yamaguchi, Yasutoshi Ida, Kenji Umakoshi, Tomohiro Inoue. 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… boone891214/sanity-check-lth — Sample code use for NeurIPS 2021 paper: Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the…