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)☆35Updated 5 years ago
- Sliced Wasserstein Distance for Learning Gaussian Mixture Models☆63Updated 2 years ago
- Hypergradient descent☆149Updated last year
- MMD, Hausdorff and Sinkhorn divergences scaled up to 1,000,000 samples.☆56Updated 6 years ago
- ☆53Updated 7 years ago
- ☆123Updated last year
- PyTorch implementation of Hessian Free optimisation☆43Updated 5 years ago
- This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).☆50Updated 4 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆61Updated 7 years ago
- Optimization with orthogonal constraints and on general manifolds☆129Updated 4 years ago
- Lipschitz Neural Networks described in "Sorting Out Lipschitz Function Approximation" (ICML 2019).☆56Updated 5 years ago
- The code for the paper: https://arxiv.org/pdf/1802.00168.pdf☆17Updated 5 years ago
- Riemannian approach to batch normalization☆22Updated 7 years ago
- Geometric Certifications of Neural Nets☆42Updated 2 years ago
- Nonlinear SVGD for Learning Diversified Mixture Models☆13Updated 6 years ago
- This repository is no longer maintained. Check☆81Updated 5 years ago
- Implementation of the Sliced Wasserstein Autoencoders☆91Updated 7 years ago
- This project is the Torch implementation of our accepted AAAI 2018 paper : orthogonal weight normalization method for solving orthogonali…☆57Updated 5 years ago
- SGD and Ordered SGD codes for deep learning, SVM, and logistic regression☆35Updated 4 years ago
- Gabriel Peyré, Marco Cuturi, Justin Solomon, Gromov-Wasserstein Averaging of Kernel and Distance Matrices, Proc. of ICML 2016.☆73Updated 8 years ago
- Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088☆53Updated 6 years ago
- Rethinking Bias-Variance Trade-off for Generalization of Neural Networks☆49Updated 4 years ago
- [JMLR] TRADES + random smoothing for certifiable robustness☆14Updated 4 years ago
- Implementation of Information Dropout☆39Updated 7 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- Monotone operator equilibrium networks☆53Updated 5 years ago
- PyTorch implementation of Neural Processes☆89Updated 6 years ago
- Logit Pairing Methods Can Fool Gradient-Based Attacks [NeurIPS 2018 Workshop on Security in Machine Learning]☆19Updated 6 years ago
- ☆87Updated 11 months ago
- Public code for a paper "Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks."☆34Updated 6 years ago