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"
  ☆64Updated 4 years ago
• avirmaux / lipEstimation
  ☆59Updated 2 years ago
• litian96 / TERM
  Tilted Empirical Risk Minimization (ICLR '21)
  ☆60Updated 2 years ago
• AnonymousNIPS2019 / DeepnetHessian
  The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size
  ☆17Updated 6 years ago
• RuntianZ / doro
  Distributional and Outlier Robust Optimization (ICML 2021)
  ☆28Updated 4 years ago
• yaodongyu / Rethink-BiasVariance-Tradeoff
  Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
  ☆50Updated 4 years ago
• AlexMeinke / certified-certain-uncertainty
  A way to achieve uniform confidence far away from the training data.
  ☆38Updated 4 years ago
• hongyanz / TRADES-smoothing
  [JMLR] TRADES + random smoothing for certifiable robustness
  ☆14Updated 5 years ago
• max-andr / provable-robustness-max-linear-regions
  Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]
  ☆31Updated 5 years ago
• duchi-lab / certifiable-distributional-robustness
  Certifying Some Distributional Robustness with Principled Adversarial Training (https://arxiv.org/abs/1710.10571)
  ☆45Updated 7 years ago
• google-research / mnist-c
  ☆69Updated 6 years ago
• davidstutz / disentangling-robustness-generalization
  CVPR'19 experiments with (on-manifold) adversarial examples.
  ☆45Updated 5 years ago
• fengliu90 / DK-for-TST
  This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).
  ☆51Updated 4 years ago
• divyam3897 / ANP_VS
  Code for the paper "Adversarial Neural Pruning with Latent Vulnerability Suppression"
  ☆15Updated 2 years ago
• jfc43 / robust-ood-detection
  Robust Out-of-distribution Detection in Neural Networks
  ☆73Updated 3 years ago
• tudor-berariu / fisher-information-matrix
  PyTorch implementation of FIM and empirical FIM
  ☆60Updated 7 years ago
• xuanqing94 / BayesianDefense
  Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
  ☆62Updated 6 years ago
• deep-lab / DeepnetHessian
  Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians (ICML 2019)
  ☆16Updated 6 years ago
• reiinakano / invariant-risk-minimization
  Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893
  ☆91Updated 5 years ago
• yangarbiter / robust-local-lipschitz
  A Closer Look at Accuracy vs. Robustness
  ☆88Updated 4 years ago
• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago
• P2333 / Max-Mahalanobis-Training
  Max Mahalanobis Training (ICML 2018 + ICLR 2020)
  ☆90Updated 4 years ago
• BorealisAI / mma_training
  Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"
  ☆34Updated 5 years ago
• dirichletcal / experiments_neurips
  ☆21Updated 2 years ago
• locuslab / projected_sinkhorn
  ☆88Updated last year
• nitarshan / robust-generalization-measures
  Official code for "In Search of Robust Measures of Generalization" (NeurIPS 2020)
  ☆28Updated 4 years ago
• igolan / bgd
  Implementation of Bayesian Gradient Descent
  ☆37Updated 2 years ago
• P2333 / Adaptive-Diversity-Promoting
  Adversarial Defense for Ensemble Models (ICML 2019)
  ☆61Updated 4 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
• cfinlay / tulip
  Scaleable input gradient regularization
  ☆22Updated 6 years ago