toshas / sttpLinks
Spectral Tensor Train Parameterization of Deep Learning Layers
☆17Updated 4 years ago
Alternatives and similar repositories for sttp
Users that are interested in sttp are comparing it to the libraries listed below
Sorting:
- Code for the article "What if Neural Networks had SVDs?", to be presented as a spotlight paper at NeurIPS 2020.☆77Updated last year
- Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness☆48Updated 4 years ago
- Monotone operator equilibrium networks☆54Updated 5 years ago
- Efficient Riemannian Optimization on Stiefel Manifold via Cayley Transform☆44Updated 6 years ago
- ☆30Updated 5 years ago
- ☆12Updated 3 years ago
- ☆59Updated 5 years ago
- Efficient Householder Transformation in PyTorch☆69Updated 4 years ago
- Optimization with orthogonal constraints and on general manifolds☆130Updated 5 years ago
- Padé Activation Units: End-to-end Learning of Activation Functions in Deep Neural Network☆64Updated 4 years ago
- Code for Understanding and Mitigating Exploding Inverses in Invertible Neural Networks (AISTATS 2021) http://arxiv.org/abs/2006.09347☆30Updated 5 years ago
- [AAAI 2020 Oral] Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution☆39Updated 5 years ago
- NeurIPS 2021, Code for Measuring Generalization with Optimal Transport☆28Updated 4 years ago
- Code for the paper: "Tensor Programs II: Neural Tangent Kernel for Any Architecture"☆106Updated 5 years ago
- ☆54Updated last year
- Code for the ICML 2021 and ICLR 2022 papers: Skew Orthogonal Convolutions, Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100☆18Updated 3 years ago
- ☆24Updated 4 years ago
- Code for "Training Deep Energy-Based Models with f-Divergence Minimization" ICML 2020☆36Updated 2 years ago
- The official code for Efficient Learning of Generative Models via Finite-Difference Score Matching☆12Updated 3 years ago
- Differentiable Optimizers with Perturbations in Pytorch☆69Updated 4 years ago
- Official Release of "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling"☆49Updated 5 years ago
- Adaptive gradient descent without descent☆51Updated 4 years ago
- Implicit networks can be trained efficiently and simply by using Jacobian-free Backprop (JFB).☆41Updated 4 years ago
- The original code for the paper "How to train your MAML" along with a replication of the original "Model Agnostic Meta Learning" (MAML) p…☆41Updated 5 years ago
- ☆47Updated 6 years ago
- ☆35Updated 4 years ago
- Estimating Gradients for Discrete Random Variables by Sampling without Replacement☆40Updated 5 years ago
- code for "Semi-Discrete Normalizing Flows through Differentiable Tessellation"☆26Updated 3 years ago
- Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)☆27Updated 6 years ago
- [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).☆59Updated 4 years ago