AdamCobb / hamiltorchLinks
PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks
☆460Updated last year
Alternatives and similar repositories for hamiltorch
Users that are interested in hamiltorch are comparing it to the libraries listed below
Sorting:
- Laplace approximations for Deep Learning.☆523Updated 6 months ago
- ☆155Updated 3 years ago
- ☆250Updated 2 years ago
- Manifold-learning flows (ℳ-flows)☆230Updated 4 years ago
- Code for the paper "Learning Differential Equations that are Easy to Solve"☆284Updated 3 years ago
- Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.☆226Updated last year
- Deep GPs built on top of TensorFlow/Keras and GPflow☆127Updated last year
- Regression datasets from the UCI repository with standardized test-train splits.☆48Updated 3 years ago
- Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.☆454Updated 2 months ago
- Normalizing flows in PyTorch☆416Updated 3 weeks ago
- This library would form a permanent home for reusable components for deep probabilistic programming. The library would form and harness a…☆309Updated 4 months ago
- Gaussian processes in JAX and Flax.☆549Updated last week
- Normalizing flows in PyTorch☆969Updated 10 months ago
- PyTorch implementation of bayesian neural network [torchbnn]☆549Updated last year
- Constrained optimization toolkit for PyTorch☆700Updated 3 months ago
- BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.☆595Updated 10 months ago
- A community repository for benchmarking Bayesian methods☆111Updated 3 years ago
- Lightweight MCMC sampling for PyTorch Models aka My Corona Project☆51Updated 3 months ago
- Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"☆170Updated 3 years ago
- Implementation of normalizing flows in TensorFlow 2 including a small tutorial.☆147Updated last month
- A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch☆633Updated 6 months ago
- Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"☆472Updated 2 years ago
- Simple (and cheap!) neural network uncertainty estimation☆76Updated last month
- Code for paper "SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows"☆288Updated 4 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆90Updated 5 years ago
- Official Implementation of "Transformers Can Do Bayesian Inference", the PFN paper☆237Updated last year
- Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.☆240Updated last year
- Gaussian process modelling in Python☆225Updated 10 months ago
- Simulation-based inference benchmark☆104Updated 9 months ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆108Updated last year