idiap / importance-sampling
Code for experiments regarding importance sampling for training neural networks
☆321Updated 3 years ago
Related projects ⓘ
Alternatives and complementary repositories for importance-sampling
- A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).☆204Updated 5 years ago
- Implements pytorch code for the Accelerated SGD algorithm.☆214Updated 6 years ago
- Neural Architecture Search with Bayesian Optimisation and Optimal Transport☆133Updated 5 years ago
- Sparse Variational Dropout, ICML 2017☆310Updated 4 years ago
- hessian in pytorch☆185Updated 4 years ago
- Totally Versatile Miscellanea for Pytorch☆468Updated 2 years ago
- Example code for the paper "Understanding deep learning requires rethinking generalization"☆177Updated 4 years ago
- Efficient PyTorch Hessian eigendecomposition tools!☆363Updated 8 months ago
- Implementations of ideas from recent papers☆391Updated 3 years ago
- ☆250Updated 7 years ago
- A drop-in replacement for CIFAR-10.☆235Updated 3 years ago
- Collection of algorithms for approximating Fisher Information Matrix for Natural Gradient (and second order method in general)☆135Updated 5 years ago
- Implementation for the Lookahead Optimizer.☆238Updated 2 years ago
- ☆153Updated 2 years ago
- ☆219Updated 6 years ago
- Release of CIFAR-10.1, a new test set for CIFAR-10.☆220Updated 4 years ago
- Code to reproduce some of the figures in the paper "On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima"☆138Updated 7 years ago
- A PyTorch library for two-sample tests☆237Updated last year
- Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832☆246Updated 6 years ago
- ☆132Updated 7 years ago
- Learning Sparse Neural Networks through L0 regularization☆240Updated 4 years ago
- Implementation of "Overcoming catastrophic forgetting in neural networks" in Tensorflow☆296Updated 4 years ago
- A plug-in replacement for DataLoader to load Imagenet disk-sequentially in PyTorch.☆238Updated 3 years ago
- Second-order optimiser for deep networks☆76Updated 6 years ago
- ☆143Updated last year
- PyTorch-SSO: Scalable Second-Order methods in PyTorch☆141Updated last year
- Contains code for the NeurIPS 2019 paper "Practical Deep Learning with Bayesian Principles"☆242Updated 4 years ago
- Code for "EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis" https://arxiv.org/abs/1905.05934☆111Updated 4 years ago