ermongroup / f-dre
Featurized Density Ratio Estimation
☆20Updated 3 years ago
Alternatives and similar repositories for f-dre:
Users that are interested in f-dre are comparing it to the libraries listed below
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- ☆53Updated 8 months ago
- PyTorch implementation of the OT-Flow approach in arXiv:2006.00104☆51Updated 8 months ago
- Noise Contrastive Estimation (NCE) in PyTorch☆32Updated 3 weeks ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- ☆23Updated 3 years ago
- Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)☆28Updated 6 years ago
- Official Release of "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling"☆49Updated 4 years ago
- [AISTATS2020] The official repository of "Invertible Generative Modling using Linear Rational Splines (LRS)".☆20Updated last year
- ☆31Updated 4 years ago
- Personal implementation of "Variational Inference with Normalizing Flows" by [Rezende, et al., 2015] in PyTorch☆22Updated 5 years ago
- NeurIPS'23: Energy Discrepancies: A Score-Independent Loss for Energy-Based Models☆15Updated 5 months ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- ☆39Updated 5 years ago
- ☆29Updated 3 years ago
- The official code for Efficient Learning of Generative Models via Finite-Difference Score Matching☆12Updated 2 years ago
- Official implementation of Transformer Neural Processes☆71Updated 2 years ago
- PyTorch implementation of Continuously Indexed Flows paper, with many baseline normalising flows☆31Updated 3 years ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Code for the paper Semi-Conditional Normalizing Flows for Semi-Supervised Learning☆28Updated 3 years ago
- Code for "Function Space Particle Optimization for Bayesian Neural Networks"☆17Updated 2 years ago
- Code for "Training Deep Energy-Based Models with f-Divergence Minimization" ICML 2020☆36Updated 2 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 5 years ago
- A PyTorch re-implementation of "Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives"☆18Updated 5 years ago
- ☆13Updated 6 years ago
- ☆38Updated 4 years ago
- Learning Generative Models across Incomparable Spaces (ICML 2019)☆27Updated 5 years ago
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆19Updated 3 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 3 years ago