Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'
☆61Jun 3, 2018Updated 8 years ago
Alternatives and similar repositories for uncertainty-adversarial-paper
Users that are interested in uncertainty-adversarial-paper are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Implementations of the ICML 2017 paper (with Yarin Gal)☆39Dec 15, 2017Updated 8 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆140Feb 24, 2016Updated 10 years ago
- Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832☆251Nov 8, 2018Updated 7 years ago
- VectorDefense: Vectorization as a Defense to Adversarial Examples --->☆13May 3, 2018Updated 8 years ago
- Bayesian Deep Learning with Edward (and a trick using Dropout)☆15Jun 7, 2017Updated 9 years ago
- Managed hosting for WordPress and PHP on Cloudways • AdManaged hosting for WordPress, Magento, Laravel, or PHP apps, on multiple cloud providers. Deploy in minutes on Cloudways by DigitalOcean.
- Code and models for our paper "Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis"☆10Aug 2, 2024Updated last year
- Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.☆47Sep 16, 2019Updated 6 years ago
- Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)☆134Jul 30, 2019Updated 6 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Apr 8, 2020Updated 6 years ago
- Facebook Post Reactions dataset☆12Dec 7, 2017Updated 8 years ago
- Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"☆583Feb 26, 2022Updated 4 years ago
- On the decision boundary of deep neural networks☆38Aug 23, 2018Updated 7 years ago
- MCMC routines☆12Nov 22, 2022Updated 3 years ago
- ☆11May 26, 2023Updated 3 years ago
- Virtual machines for every use case on DigitalOcean • AdGet dependable uptime with 99.99% SLA, simple security tools, and predictable monthly pricing with DigitalOcean's virtual machines, called Droplets.
- Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network☆61Jun 25, 2019Updated 7 years ago
- Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features☆24Apr 20, 2019Updated 7 years ago
- Uncertainty interpretations of the neural network☆32May 3, 2018Updated 8 years ago
- ☆11Feb 15, 2023Updated 3 years ago
- ☆53May 4, 2018Updated 8 years ago
- Generating Natural Adversarial Examples, ICLR 2018☆143May 17, 2018Updated 8 years ago
- ☆13Feb 17, 2018Updated 8 years ago
- An empirical investigation of deep learning theory☆16Oct 3, 2019Updated 6 years ago
- Image Super-Resolution as a Defense Against Adversarial Attacks☆91Jan 17, 2019Updated 7 years ago
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- ☆10Nov 5, 2016Updated 9 years ago
- Official code for "Efficient Deep Gaussian Process Models for Variable-Sized Inputs" - accepted in IJCNN2019☆15Jul 17, 2019Updated 7 years ago
- Implementation in pytorch of SR-NMT https://arxiv.org/abs/1805.04185v1☆26Jun 8, 2018Updated 8 years ago
- DNN Inference with CPU, C++, ONNX support: Instant☆56Oct 17, 2018Updated 7 years ago
- Bayesian Deep Learning Benchmarks☆673Mar 24, 2023Updated 3 years ago
- Uncertainty estimation on Mnist dataset☆23Jan 13, 2018Updated 8 years ago
- Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch☆49Nov 3, 2017Updated 8 years ago
- f-GANs in an Information Geometric Nutshell☆13Jun 21, 2017Updated 9 years ago
- Evaluation dataset for Neural Point-Based Graphics☆12Dec 10, 2019Updated 6 years ago
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- Deep Manifold Traversal☆14Nov 14, 2016Updated 9 years ago
- ☆18Dec 31, 2018Updated 7 years ago
- PyTorch Lightning based framework to run experiments for self-supervised learning tasks.☆10Feb 14, 2020Updated 6 years ago
- Meta-learning learning rates with higher☆12Sep 27, 2019Updated 6 years ago
- Analysis of Adversarial Logit Pairing☆61Aug 13, 2018Updated 7 years ago
- ☆25Aug 11, 2022Updated 3 years ago
- ☆15Oct 19, 2020Updated 5 years ago