emanuele / kernel_two_sample_testLinks
A python implementation of the kernel two-samples test as in Gretton et al 2012 (JMLR).
☆34Updated 9 years ago
Alternatives and similar repositories for kernel_two_sample_test
Users that are interested in kernel_two_sample_test are comparing it to the libraries listed below
Sorting:
- Interpretation of Neural Network is Fragile☆36Updated last year
- Related materials for robust and explainable machine learning☆48Updated 7 years ago
- Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)☆62Updated 6 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆61Updated 7 years ago
- Learning kernels to maximize the power of MMD tests☆211Updated 7 years ago
- Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network☆62Updated 6 years ago
- Public code for a paper "Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks."☆35Updated 6 years ago
- Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]☆31Updated 5 years ago
- Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing the…☆55Updated 3 years ago
- Code for Invariant Rep. Without Adversaries (NIPS 2018)☆35Updated 5 years ago
- Code for "Detecting Adversarial Samples from Artifacts" (Feinman et al., 2017)☆111Updated 7 years ago
- Code for "Robustness May Be at Odds with Accuracy"☆91Updated 2 years ago
- ☆88Updated last year
- Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.☆49Updated 6 years ago
- Code for the Adversarial Image Detectors and a Saliency Map☆12Updated 8 years ago
- Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893☆91Updated 5 years ago
- The Ultimate Reference for Out of Distribution Detection with Deep Neural Networks☆118Updated 5 years ago
- ☆13Updated 7 years ago
- Certifying Some Distributional Robustness with Principled Adversarial Training (https://arxiv.org/abs/1710.10571)☆45Updated 7 years ago
- Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"☆228Updated 6 years ago
- Example code for the paper "Understanding deep learning requires rethinking generalization"☆178Updated 5 years ago
- Geometric Certifications of Neural Nets☆42Updated 3 years ago
- This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).☆51Updated 4 years ago
- ☆13Updated 5 years ago
- Ensemble Adversarial Training on MNIST☆121Updated 8 years ago
- Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation [NeurIPS 2017]☆18Updated 7 years ago
- Implementation of the variational continual learning method☆195Updated 6 years ago
- ☆146Updated 8 years ago
- Explaining Image Classifiers by Counterfactual Generation☆28Updated 3 years ago
- NIPS Adversarial Vision Challenge☆41Updated 7 years ago