FrederikWarburg / LaplaceAELinks
☆33Updated 3 years ago
Alternatives and similar repositories for LaplaceAE
Users that are interested in LaplaceAE are comparing it to the libraries listed below
Sorting:
- Code for "Generalised Implicit Neural Representations" (NeurIPS 2022).☆71Updated 2 years ago
- Generative Modeling with Optimal Transport Maps - ICLR 2022☆61Updated 3 years ago
- PyTorch implementation of the OT-Flow approach in arXiv:2006.00104☆57Updated last year
- Repository for the paper "Riemannian Laplace approximations for Bayesian neural networks"☆11Updated 2 years ago
- Code for the paper "Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification" (2020)☆43Updated last year
- Code for "Variational Autoencoder with Learned Latent Structure"☆34Updated 4 years ago
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)☆78Updated 3 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆78Updated 2 years ago
- ☆28Updated 3 years ago
- Official Code: Estimating Model Uncertainty of Neural Networks in Sparse Information Form, ICML2020.☆31Updated 4 years ago
- NeurIPS23 "Flow Factorized Representation Learning"☆41Updated 2 weeks ago
- A PyTorch Implementation of Density Estimation Using Real NVP☆82Updated 4 years ago
- ☆24Updated 3 years ago
- This repository contains code for applying Riemannian geometry in machine learning.☆78Updated 4 years ago
- Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".☆37Updated 3 years ago
- Code for experiments to learn uncertainty☆30Updated 2 years ago
- PyTorch implementation of "Wasserstein-2 Generative Networks" (ICLR 2021)☆57Updated 2 years ago
- Image-to-image regression with uncertainty quantification in PyTorch. Take any dataset and train a model to regress images to images with…☆59Updated 2 years ago
- Pytorch implementation of Generative Models as Distributions of Functions 🌿☆152Updated 2 years ago
- Implementation of "Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains" by Tancik et al.☆109Updated 2 weeks ago
- ☆54Updated last year
- beta-NLL introduced in our paper "On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks" ICLR 2022☆46Updated 3 years ago
- Package for working with hypernetworks in PyTorch.☆131Updated 2 years ago
- ☆32Updated 5 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- 🚀 A powerful library for efficient training of Neural Fields at scale.☆29Updated last year
- Free-form flows are a generative model training a pair of neural networks via maximum likelihood☆50Updated 6 months ago
- ☆64Updated last year
- PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)☆25Updated 3 years ago
- Code for "On the Spectral Bias of Neural Networks", to appear in ICML 2019 (Long Beach, CA).☆113Updated 6 years ago