moskomule / l0.pytorchLinks
an implementation of L0 regularization with PyTorch
☆57Updated 7 years ago
Alternatives and similar repositories for l0.pytorch
Users that are interested in l0.pytorch are comparing it to the libraries listed below
Sorting:
- Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch☆49Updated 8 years ago
- PyTorch Implementations of Dropout Variants☆88Updated 7 years ago
- Implementation of the reversible residual network in pytorch☆106Updated 3 years ago
- Hypergradient descent☆147Updated last year
- Learning Sparse Neural Networks through L0 regularization☆245Updated 5 years ago
- This repository is no longer maintained. Check☆81Updated 5 years ago
- Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088☆60Updated 6 years ago
- Sparse Variational Dropout, ICML 2017☆312Updated 5 years ago
- A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).☆206Updated 7 years ago
- A pytorch implementation of our jacobian regularizer to encourage learning representations more robust to input perturbations.☆129Updated 2 years ago
- Pytorch implementation of Variational Dropout Sparsifies Deep Neural Networks☆84Updated 4 years ago
- This project is the Torch implementation of our accepted AAAI 2018 paper : orthogonal weight normalization method for solving orthogonali…☆57Updated 6 years ago
- Uncertainty Estimation via Stochastic Batch Normalization☆20Updated 7 years ago
- A machine learning library for PyTorch☆94Updated 3 years ago
- Code for reproducing Flow ++ experiments☆190Updated 6 years ago
- Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)☆27Updated 6 years ago
- PyTorch Framework for Developing Memory Efficient Deep Invertible Networks☆256Updated 2 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆112Updated 7 years ago
- [NeurIPS '18] "Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?" Official Implementation.☆130Updated 3 years ago
- Implementation of the Sliced Wasserstein Autoencoder using PyTorch☆101Updated 7 years ago
- Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)☆36Updated 5 years ago
- Implements pytorch code for the Accelerated SGD algorithm.☆215Updated 7 years ago
- Code for Variational Laplace Autoencoders☆54Updated 2 years ago
- hessian in pytorch☆187Updated 5 years ago
- Code for "EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis" https://arxiv.org/abs/1905.05934☆113Updated 5 years ago
- Mode Connectivity and Fast Geometric Ensembles in PyTorch☆282Updated 3 years ago
- Example code for the paper "Understanding deep learning requires rethinking generalization"☆178Updated 5 years ago
- Memory efficient MAML using gradient checkpointing☆86Updated 5 years ago
- Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019☆39Updated 5 years ago
- Code for "Stochastic Optimization of Sorting Networks using Continuous Relaxations", ICLR 2019.☆148Updated 2 years ago