siplab-gt / VAELLSLinks
Code for "Variational Autoencoder with Learned Latent Structure"
☆34Updated 4 years ago
Alternatives and similar repositories for VAELLS
Users that are interested in VAELLS are comparing it to the libraries listed below
Sorting:
- ☆38Updated 5 years ago
- ☆54Updated last year
- This repository contains code for applying Riemannian geometry in machine learning.☆77Updated 4 years ago
- [NeurIPS 2020] Neural Manifold Ordinary Differential Equations (https://arxiv.org/abs/2006.10254)☆119Updated 2 years ago
- Spatio-temporal alignements: Optimal transport in space and time☆47Updated 4 months ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- Gaussian Process Prior Variational Autoencoder☆84Updated 6 years ago
- PyTorch implementation of the OT-Flow approach in arXiv:2006.00104☆56Updated last year
- Riemannian Convex Potential Maps☆67Updated 2 years ago
- Pytorch implementation for "Particle Flow Bayes' Rule"☆14Updated 6 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆74Updated 3 years ago
- ICML 2020 Paper: Latent Variable Modelling with Hyperbolic Normalizing Flows☆54Updated 2 years ago
- ☆123Updated 2 years ago
- Learning the optimal transport map via input convex neural neworks☆41Updated 5 years ago
- Reproducing the paper "Variational Sparse Coding" for the ICLR 2019 Reproducibility Challenge☆62Updated 2 years ago
- Learning Generative Models across Incomparable Spaces (ICML 2019)☆27Updated 5 years ago
- PyTorch implementation of Algorithm 1 of "On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models"☆38Updated last year
- ☆63Updated last year
- Experiments for Meta-Learning Symmetries by Reparameterization☆57Updated 4 years ago
- Official implementation of the paper "Topographic VAEs learn Equivariant Capsules"☆80Updated 3 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
- Python implementation of smooth optimal transport.☆60Updated 4 years ago
- Official Release of "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling"☆49Updated 5 years ago
- ☆47Updated last year
- ☆32Updated 2 years ago
- Code for Sliced Gromov-Wasserstein☆69Updated 5 years ago
- Algorithms for computations on random manifolds made easier☆92Updated last year
- Implementation of the Convolutional Conditional Neural Process☆125Updated 4 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆76Updated last year