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ganguli-lab/degrees-of-freedom

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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.

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Questions fréquentes

Que fait 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).

Quelles sont les fonctionnalités principales de ganguli-lab/degrees-of-freedom ?

Les fonctionnalités principales de ganguli-lab/degrees-of-freedom sont : Weight Pruning.

Quelles sont les alternatives open-source à ganguli-lab/degrees-of-freedom ?

Les alternatives open-source à ganguli-lab/degrees-of-freedom incluent : 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…