HanxiSun / SteinNS
code for Stein Neural Sampler
☆22Updated 5 years ago
Related projects: ⓘ
- Reference implementation of divergence triangle https://arxiv.org/abs/1812.10907☆16Updated last year
- Code for "MetaFun: Meta-Learning with Iterative Functional Updates"☆14Updated 4 years ago
- PyTorch implementation of Algorithm 1 of "On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models"☆36Updated last month
- The official code for Efficient Learning of Generative Models via Finite-Difference Score Matching☆12Updated last year
- Code for "A Spectral Approach to Gradient Estimation for Implicit Distributions" (ICML'18)☆32Updated last year
- A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…☆52Updated 3 months ago
- Stochastic algorithms for computing Regularized Optimal Transport☆55Updated 6 years ago
- Featurized Density Ratio Estimation☆19Updated 3 years ago
- SVGD implementation☆10Updated 6 years ago
- Code for reproducing results in the sliced score matching paper (UAI 2019)☆138Updated 4 years ago
- A PyTorch Implementation of the Importance Weighted Autoencoders☆38Updated 5 years ago
- Code release for the ICLR paper☆20Updated 6 years ago
- A tensorflow implementation of the NIPS 2018 paper "Variational Inference with Tail-adaptive f-Divergence"☆20Updated 5 years ago
- ☆49Updated 3 years ago
- Implicit Generation and Generalization in Energy Based Models in PyTorch☆65Updated 5 years ago
- PyTorch implementation of the OT-Flow approach in arXiv:2006.00104☆49Updated last month
- Official Release of "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling"☆45Updated 4 years ago
- Code required to reproduce the experiments in Auxiliary Variational MCMC☆17Updated 5 years ago
- ☆62Updated 7 months ago
- ☆31Updated 3 years ago
- ☆10Updated 6 years ago
- [ICML'21] Improved Contrastive Divergence Training of Energy Based Models☆60Updated 2 years ago
- Code for "Training Deep Energy-Based Models with f-Divergence Minimization" ICML 2020☆35Updated last year
- Density Ratio Estimation via Infinitesimal Classification (AISTATS 2022 Oral)☆16Updated 2 years ago
- ☆26Updated 5 years ago
- Sliced Wasserstein Generator☆23Updated 5 years ago
- ☆35Updated this week
- Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)☆28Updated 5 years ago
- ☆22Updated 2 years ago
- Code for the paper Semi-Conditional Normalizing Flows for Semi-Supervised Learning☆28Updated 3 years ago