ShinKyuY / Understanding_Black_box_Predictions_via_Influence_Functions_tutorial_MNISTLinks
Tiny Tutorial on https://arxiv.org/abs/1703.04730
☆13Updated 5 years ago
Alternatives and similar repositories for Understanding_Black_box_Predictions_via_Influence_Functions_tutorial_MNIST
Users that are interested in Understanding_Black_box_Predictions_via_Influence_Functions_tutorial_MNIST are comparing it to the libraries listed below
Sorting:
- Code for the paper "Understanding Generalization through Visualizations"☆61Updated 4 years ago
- Tilted Empirical Risk Minimization (ICLR '21)☆59Updated last year
- Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]☆32Updated 5 years ago
- Rethinking Bias-Variance Trade-off for Generalization of Neural Networks☆49Updated 4 years ago
- ☆22Updated 6 years ago
- The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size☆17Updated 6 years ago
- A way to achieve uniform confidence far away from the training data.☆38Updated 4 years ago
- This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).☆50Updated 4 years ago
- ☆59Updated 2 years ago
- [JMLR] TRADES + random smoothing for certifiable robustness☆14Updated 4 years ago
- CVPR'19 experiments with (on-manifold) adversarial examples.☆45Updated 5 years ago
- Scaleable input gradient regularization☆22Updated 6 years ago
- ☆21Updated 2 years ago
- A Closer Look at Accuracy vs. Robustness☆89Updated 4 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- Computing various measures and generalization bounds on convolutional and fully connected networks☆35Updated 6 years ago
- Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network☆62Updated 6 years ago
- Code for the Paper 'On the Connection Between Adversarial Robustness and Saliency Map Interpretability' by C. Etmann, S. Lunz, P. Maass, …☆16Updated 6 years ago
- Code for the paper "Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers" published in ICLR 2019☆13Updated 6 years ago
- Adversarial Defense for Ensemble Models (ICML 2019)☆61Updated 4 years ago
- Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"☆34Updated 5 years ago
- Max Mahalanobis Training (ICML 2018 + ICLR 2020)☆90Updated 4 years ago
- Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893☆89Updated 5 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆61Updated 7 years ago
- This repo contains the code for the experiments in "Rademacher Complexity for Adversarially Robust Generalization"☆9Updated 6 years ago
- ☆88Updated 11 months ago
- PyTorch implementation of FIM and empirical FIM☆61Updated 7 years ago
- Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians (ICML 2019)☆17Updated 6 years ago
- Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.☆49Updated 5 years ago
- Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing the…☆55Updated 2 years ago