omihub777 / MLP-Mixer-CIFAR
PyTorch implementation of Mixer-nano (#parameters is 0.67M, originally Mixer-S/16 has 18M) with 90.83 % acc. on CIFAR-10. Training from scratch.
☆29Updated 3 years ago
Alternatives and similar repositories for MLP-Mixer-CIFAR:
Users that are interested in MLP-Mixer-CIFAR are comparing it to the libraries listed below
- Lightweight torch implementation of rigl, a sparse-to-sparse optimizer.☆56Updated 3 years ago
- Code for "Picking Winning Tickets Before Training by Preserving Gradient Flow" https://openreview.net/pdf?id=SkgsACVKPH☆102Updated 4 years ago
- Soft Threshold Weight Reparameterization for Learnable Sparsity☆88Updated last year
- EP☆15Updated 3 years ago
- Python implementation of the methods in Meulemans et al. 2020 - A Theoretical Framework For Target Propagation☆30Updated 2 months ago
- ☆156Updated 2 years ago
- Implementation of Continuous Sparsification, a method for pruning and ticket search in deep networks☆32Updated 2 years ago
- This is the official implementation of the ICML 2023 paper - Can Forward Gradient Match Backpropagation ?☆12Updated last year
- ☆188Updated 4 years ago
- ☆62Updated last month
- Train ImageNet *fast* in 500 lines of code with FFCV☆136Updated 8 months ago
- ☆16Updated 2 years ago
- PyTorch implementation for Vision Transformer[Dosovitskiy, A.(ICLR'21)] modified to obtain over 90% accuracy FROM SCRATCH on CIFAR-10 wit…☆176Updated 11 months ago
- {KFAC,EKFAC,Diagonal,Implicit} Fisher Matrices and finite width NTKs in PyTorch☆210Updated 3 months ago
- Pytorch implementation of KFAC and E-KFAC (Natural Gradient).☆130Updated 5 years ago
- Code for Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot☆42Updated 4 years ago
- Implementation of "Gradients without backpropagation" paper (https://arxiv.org/abs/2202.08587) using functorch☆108Updated last year
- Code accompanying the NeurIPS 2020 paper: WoodFisher (Singh & Alistarh, 2020)☆48Updated 3 years ago
- Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians (ICML 2019)☆17Updated 5 years ago
- Code release for REPAIR: REnormalizing Permuted Activations for Interpolation Repair☆46Updated 11 months ago
- ☆57Updated last year
- ☆199Updated last year
- Efficient Riemannian Optimization on Stiefel Manifold via Cayley Transform☆37Updated 5 years ago
- Study on the applicability of Direct Feedback Alignment to neural view synthesis, recommender systems, geometric learning, and natural la…☆86Updated 2 years ago
- Towards Understanding Sharpness-Aware Minimization [ICML 2022]☆35Updated 2 years ago
- Code for "Structured Sparsity Inducing Adaptive Optimizers for Deep Learning" in PyTorch☆18Updated 3 years ago
- Prospect Pruning: Finding Trainable Weights at Initialization Using Meta-Gradients☆31Updated 2 years ago
- Public code for Illing, Ventura, Bellec & Gerstner 2021: Local plasticity rules can learn deep representations using self-supervised cont…☆24Updated 8 months ago
- Code for the paper: "Tensor Programs II: Neural Tangent Kernel for Any Architecture"☆102Updated 4 years ago
- Hessian spectral density estimation in TF and Jax☆120Updated 4 years ago