martius-lab / beta-nllLinks
beta-NLL introduced in our paper "On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks" ICLR 2022
☆45Updated 3 years ago
Alternatives and similar repositories for beta-nll
Users that are interested in beta-nll are comparing it to the libraries listed below
Sorting:
- All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)☆20Updated 3 years ago
- Official Implementation of "Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions" (ICLR, …☆87Updated 2 years ago
- Neural Diffusion Processes☆81Updated last year
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)☆78Updated 3 years ago
- Supplementary material to reproduce "The Unreasonable Effectiveness of Deep Evidential Regression"☆29Updated 2 years ago
- ☆32Updated 3 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- Free-form flows are a generative model training a pair of neural networks via maximum likelihood☆49Updated 4 months ago
- This repository contains an official implementation of LPBNN.☆38Updated 2 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆77Updated 2 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 3 years ago
- Implementation of the Convolutional Conditional Neural Process☆125Updated 4 years ago
- The Wasserstein Distance and Optimal Transport Map of Gaussian Processes☆52Updated 5 years ago
- Experiments for Neural Flows paper☆98Updated 3 years ago
- Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".☆36Updated 3 years ago
- Official implementation of Transformer Neural Processes☆78Updated 3 years ago
- A library for uncertainty quantification based on PyTorch☆122Updated 3 years ago
- Code for experiments to learn uncertainty☆30Updated 2 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆89Updated 5 years ago
- ☆64Updated last year
- Repo for our paper "Repulsive deep ensembles are Bayesian"☆19Updated 3 years ago
- Official implementation of "How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?", TMLR 2023.☆18Updated last year
- ☆13Updated 2 years ago
- Code for the paper "Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift"☆22Updated 2 years ago
- A tutorial for the 2018 paper Accurate Uncertainties for Deep Learning Using Calibrated Regression by Kuleshov et al.☆52Updated 5 years ago
- Normalizing Flows with a resampled base distribution☆47Updated 3 years ago
- Repository for Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification (NeurIPS 2024)☆44Updated 11 months ago
- Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)☆38Updated 3 years ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆24Updated 2 years ago
- Official Implementation of the paper: "A Rate-Distorion View of Uncertainty Quantification", ICML 2024☆28Updated last year