wangkua1 / apd_publicLinks
Code for "Adversarial Distillation of Bayesian Neural Network Posteriors" https://arxiv.org/abs/1806.10317
☆15Updated 6 years ago
Alternatives and similar repositories for apd_public
Users that are interested in apd_public are comparing it to the libraries listed below
Sorting:
- Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019☆39Updated 5 years ago
- ☆13Updated 7 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 6 years ago
- Computing various norms/measures on over-parametrized neural networks☆49Updated 6 years ago
- Implementation of the paper "Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory", Ron Amit and Ron Meir, ICML 2018☆22Updated 5 years ago
- Example code for the paper "Understanding deep learning requires rethinking generalization"☆178Updated 5 years ago
- Principled Detection of Out-of-Distribution Examples in Neural Networks☆202Updated 8 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆61Updated 7 years ago
- Overcoming Catastrophic Forgetting by Incremental Moment Matching (IMM)☆35Updated 7 years ago
- A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)☆175Updated 7 years ago
- Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch☆49Updated 7 years ago
- PyTorch Implementation of Neural Statistician☆60Updated 3 years ago
- OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch)☆62Updated last year
- This repo provides code used in the paper "Predicting with High Correlation Features" (https://arxiv.org/abs/1910.00164):☆54Updated 3 months ago
- A machine learning library for PyTorch☆93Updated 2 years ago
- Public code for a paper "Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks."☆34Updated 6 years ago
- Related materials for robust and explainable machine learning☆48Updated 7 years ago
- Implementation of iterative inference in deep latent variable models☆43Updated 5 years ago
- Implementation of Information Dropout☆39Updated 8 years ago
- Unofficial pytorch implementation of a paper, Distributional Smoothing with Virtual Adversarial Training [Miyato+, ICLR2016].☆26Updated 7 years ago
- Tensorflow implementation of DropMax: Adaptive Variational Softmax (NeurIPS2018)☆18Updated 5 years ago
- Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)☆35Updated 5 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"☆121Updated 6 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆66Updated 5 years ago
- Implementation of Bayesian Gradient Descent☆37Updated last year
- Scaled MMD GAN☆36Updated 5 years ago
- Tensorflow implementation of the `intelligent synapse' model from [Zenke et al., (2017)] and application to the Permuted MNIST benchmark.☆22Updated 8 years ago
- Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)☆28Updated 6 years ago