uber-research / deconstructing-lottery-ticketsLinks
☆144Updated 2 years ago
Alternatives and similar repositories for deconstructing-lottery-tickets
Users that are interested in deconstructing-lottery-tickets are comparing it to the libraries listed below
Sorting:
- Discovering Neural Wirings (https://arxiv.org/abs/1906.00586)☆137Updated 5 years ago
- End-to-end training of sparse deep neural networks with little-to-no performance loss.☆323Updated 2 years ago
- SNIP: SINGLE-SHOT NETWORK PRUNING☆31Updated 4 months ago
- ☆157Updated 3 years ago
- Code for "Picking Winning Tickets Before Training by Preserving Gradient Flow" https://openreview.net/pdf?id=SkgsACVKPH☆105Updated 5 years ago
- Implementation for the Lookahead Optimizer.☆241Updated 3 years ago
- ☆191Updated 4 years ago
- Code for "EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis" https://arxiv.org/abs/1905.05934☆113Updated 5 years ago
- This repository contains code to replicate the experiments given in NeurIPS 2019 paper "One ticket to win them all: generalizing lottery …☆51Updated 11 months ago
- 🧀 Pytorch code for the Fromage optimiser.☆124Updated last year
- Mode Connectivity and Fast Geometric Ensembles in PyTorch☆272Updated 2 years ago
- ☆83Updated 5 years ago
- ☆226Updated 11 months ago
- Memory efficient MAML using gradient checkpointing☆85Updated 5 years ago
- Sparse learning library and sparse momentum resources.☆381Updated 3 years ago
- A Re-implementation of Fixed-update Initialization☆153Updated 6 years ago
- Code for paper "SWALP: Stochastic Weight Averaging forLow-Precision Training".☆62Updated 6 years ago
- [ICLR 2020] Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks☆138Updated 4 years ago
- Code for "Supermasks in Superposition"☆124Updated last year
- ☆70Updated 5 years ago
- This repository is no longer maintained. Check☆81Updated 5 years ago
- Efficient PyTorch Hessian eigendecomposition tools!☆374Updated last year
- Release of CIFAR-10.1, a new test set for CIFAR-10.☆223Updated 5 years ago
- This repository contains the results for the paper: "Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers"☆180Updated 3 years ago
- Hypergradient descent☆149Updated last year
- SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY☆114Updated 6 years ago
- Train ImageNet in 18 minutes on AWS☆130Updated last year
- NTK reading group☆87Updated 5 years ago
- A drop-in replacement for CIFAR-10.☆241Updated 4 years ago
- A small library implementing magnitude-based pruning in PyTorch☆28Updated 6 years ago