BaoWangMath / LaplacianSmoothing-GradientDescentLinks
The code for the paper: https://arxiv.org/abs/1806.06317
☆24Updated 6 years ago
Alternatives and similar repositories for LaplacianSmoothing-GradientDescent
Users that are interested in LaplacianSmoothing-GradientDescent are comparing it to the libraries listed below
Sorting:
- Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)☆36Updated 5 years ago
- Lipschitz Neural Networks described in "Sorting Out Lipschitz Function Approximation" (ICML 2019).☆58Updated 5 years ago
- Geometric Certifications of Neural Nets☆42Updated 3 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆62Updated 7 years ago
- Implementation of the Sliced Wasserstein Autoencoders☆92Updated 7 years ago
- Hypergradient descent☆147Updated last year
- Optimization with orthogonal constraints and on general manifolds☆130Updated 5 years ago
- ☆126Updated last year
- A pytorch implementation of our jacobian regularizer to encourage learning representations more robust to input perturbations.☆129Updated 2 years ago
- Implementation of Information Dropout☆39Updated 8 years ago
- ☆53Updated 7 years ago
- This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).☆53Updated 4 years ago
- Learning perturbation sets for robust machine learning☆65Updated 4 years ago
- The code for the paper: https://arxiv.org/pdf/1802.00168.pdf☆17Updated 6 years ago
- This repository is no longer maintained. Check☆81Updated 5 years ago
- Limitations of the Empirical Fisher Approximation☆49Updated 10 months ago
- PyTorch implementation of Hessian Free optimisation☆43Updated 6 years ago
- Logit Pairing Methods Can Fool Gradient-Based Attacks [NeurIPS 2018 Workshop on Security in Machine Learning]☆19Updated 7 years ago
- ☆59Updated 2 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- Implementation of the Sliced Wasserstein Autoencoder using PyTorch☆101Updated 7 years ago
- Public code for a paper "Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks."☆35Updated 7 years ago
- Monotone operator equilibrium networks☆54Updated 5 years ago
- [JMLR] TRADES + random smoothing for certifiable robustness☆14Updated 5 years ago
- MMD, Hausdorff and Sinkhorn divergences scaled up to 1,000,000 samples.☆57Updated 6 years ago
- Tilted Empirical Risk Minimization (ICLR '21)☆60Updated 2 years ago
- Notebooks for IPAM Tutorial, March 15 2019☆24Updated 6 years ago
- [NeurIPS'19] [PyTorch] Adaptive Regularization in NN☆68Updated 6 years ago
- ☆88Updated last year
- PyTorch implementation of Neural Processes☆88Updated 6 years ago