titu1994 / pytorch_odeganLinks
Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary Differential Equations
☆16Updated 4 years ago
Alternatives and similar repositories for pytorch_odegan
Users that are interested in pytorch_odegan are comparing it to the libraries listed below
Sorting:
- Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation☆68Updated 4 years ago
- Implementation of different Normalizing Flows, NF, Planar Flows, IAF, etc.☆29Updated 7 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 4 years ago
- Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning☆33Updated 5 years ago
- Experiments with Neural Ordinary Differential Equations on image and text classification tasks☆33Updated 6 years ago
- A minimal pytorch implementation of VAE, IWAE, MIWAE☆48Updated 2 years ago
- Code for paper "Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow"☆19Updated 5 years ago
- ☆29Updated 3 years ago
- Computing the eigenvalues of Neural Tangent Kernel and Conjugate Kernel (aka NNGP kernel) over the boolean cube☆47Updated 5 years ago
- Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…☆32Updated 5 years ago
- Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)☆28Updated 6 years ago
- Code base for SRSGD.☆29Updated 5 years ago
- Official PyTorch BIVA implementation (BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling)☆84Updated 2 years ago
- Structured matrices for compressing neural networks☆67Updated last year
- Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch☆49Updated 7 years ago
- ☆12Updated 5 years ago
- Uncertainty Autoencoders, AISTATS 2019☆55Updated 6 years ago
- ☆117Updated last year
- Official Release of "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling"☆49Updated 5 years ago
- Computing various norms/measures on over-parametrized neural networks☆49Updated 6 years ago
- Code for "Accelerating Natural Gradient with Higher-Order Invariance"☆30Updated 6 years ago
- Pytorch implementation of Variational Dropout Sparsifies Deep Neural Networks☆83Updated 3 years ago
- Adversarial Non-linear Independent Component Analysis☆50Updated 7 years ago
- [NeurIPS'19] [PyTorch] Adaptive Regularization in NN☆68Updated 5 years ago
- Source code for ICLR 2019 paper☆24Updated 4 years ago
- repo for paper: Adaptive Checkpoint Adjoint (ACA) method for gradient estimation in neural ODE☆56Updated 4 years ago
- BIVA: A Very Deep Hierarchy of Latent Variables forGenerative Modeling☆29Updated 5 years ago
- Official implementation of "On GANs and GMMs"☆66Updated 5 years ago
- Sliced Wasserstein Generator☆23Updated 6 years ago
- Code for reproducing results in "Generative Model with Dynamic Linear Flow"☆71Updated 6 years ago