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

• wronnyhuang / gen-viz
  Code for the paper "Understanding Generalization through Visualizations"
  ☆61Updated 4 years ago
• litian96 / TERM
  Tilted Empirical Risk Minimization (ICLR '21)
  ☆59Updated last year
• max-andr / provable-robustness-max-linear-regions
  Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]
  ☆32Updated 5 years ago
• yaodongyu / Rethink-BiasVariance-Tradeoff
  Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
  ☆49Updated 4 years ago
• ermongroup / lag-fairness
  ☆22Updated 6 years ago
• AnonymousNIPS2019 / DeepnetHessian
  The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size
  ☆17Updated 6 years ago
• AlexMeinke / certified-certain-uncertainty
  A way to achieve uniform confidence far away from the training data.
  ☆38Updated 4 years ago
• fengliu90 / DK-for-TST
  This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).
  ☆50Updated 4 years ago
• avirmaux / lipEstimation
  ☆59Updated 2 years ago
• hongyanz / TRADES-smoothing
  [JMLR] TRADES + random smoothing for certifiable robustness
  ☆14Updated 4 years ago
• davidstutz / disentangling-robustness-generalization
  CVPR'19 experiments with (on-manifold) adversarial examples.
  ☆45Updated 5 years ago
• cfinlay / tulip
  Scaleable input gradient regularization
  ☆22Updated 6 years ago
• dirichletcal / experiments_neurips
  ☆21Updated 2 years ago
• yangarbiter / robust-local-lipschitz
  A Closer Look at Accuracy vs. Robustness
  ☆89Updated 4 years ago
• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago
• bneyshabur / generalization-bounds
  Computing various measures and generalization bounds on convolutional and fully connected networks
  ☆35Updated 6 years ago
• xuanqing94 / BayesianDefense
  Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
  ☆62Updated 6 years ago
• cetmann / robustness-interpretability
  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
• geifmany / uncertainty_ICLR
  Code for the paper "Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers" published in ICLR 2019
  ☆13Updated 6 years ago
• P2333 / Adaptive-Diversity-Promoting
  Adversarial Defense for Ensemble Models (ICML 2019)
  ☆61Updated 4 years ago
• BorealisAI / mma_training
  Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"
  ☆34Updated 5 years ago
• P2333 / Max-Mahalanobis-Training
  Max Mahalanobis Training (ICML 2018 + ICLR 2020)
  ☆90Updated 4 years ago
• reiinakano / invariant-risk-minimization
  Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893
  ☆89Updated 5 years ago
• lsgos / uncertainty-adversarial-paper
  Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'
  ☆61Updated 7 years ago
• dongyin92 / adversarially-robust-generalization
  This repo contains the code for the experiments in "Rademacher Complexity for Adversarially Robust Generalization"
  ☆9Updated 6 years ago
• locuslab / projected_sinkhorn
  ☆88Updated 11 months ago
• tudor-berariu / fisher-information-matrix
  PyTorch implementation of FIM and empirical FIM
  ☆61Updated 7 years ago
• deep-lab / DeepnetHessian
  Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians (ICML 2019)
  ☆17Updated 6 years ago
• MadryLab / adversarial_spatial
  Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.
  ☆49Updated 5 years ago
• dtak / adversarial-robustness-public
  Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing the…
  ☆55Updated 2 years ago