SamsungLabs / pytorch-ensembles
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020
☆235Updated last year
Related projects ⓘ
Alternatives and complementary repositories for pytorch-ensembles
- Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"☆268Updated 2 years ago
- ☆226Updated 4 years ago
- This repository is the code for Predictive Uncertainty Estimation using Deep Ensemble☆150Updated 2 years ago
- Bayesianize: A Bayesian neural network wrapper in pytorch☆87Updated 6 months ago
- Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".☆111Updated 2 years ago
- Reusable BatchBALD implementation☆74Updated 8 months ago
- Code for the paper "Calibrating Deep Neural Networks using Focal Loss"☆155Updated 10 months ago
- Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.☆53Updated last year
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning☆92Updated 4 years ago
- Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"☆453Updated last year
- Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty☆129Updated last year
- ☆235Updated last year
- Calibration of Convolutional Neural Networks☆158Updated last year
- Contains code for the NeurIPS 2019 paper "Practical Deep Learning with Bayesian Principles"☆242Updated 4 years ago
- Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlig…☆143Updated 2 years ago
- Last-layer Laplace approximation code examples☆81Updated 3 years ago
- Project site for "Your Classifier is Secretly an Energy-Based Model and You Should Treat it Like One"☆417Updated 2 years ago
- A pytorch implementation of our jacobian regularizer to encourage learning representations more robust to input perturbations.☆123Updated last year
- Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)☆201Updated 2 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆86Updated 4 years ago
- Original PyTorch implementation of Uncertainty-guided Continual Learning with Bayesian Neural Networks, ICLR 2020☆73Updated 3 years ago
- ☆65Updated 4 years ago
- Pytorch implementation of Neural Processes for functions and images☆225Updated 2 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆72Updated last year
- PyTorch implementation of "Weight Uncertainty in Neural Networks"☆170Updated 2 years ago
- Mode Connectivity and Fast Geometric Ensembles in PyTorch☆265Updated 2 years ago
- Rethinking Bias-Variance Trade-off for Generalization of Neural Networks☆49Updated 3 years ago
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)☆74Updated 2 years ago
- Hypergradient descent☆138Updated 5 months ago
- Belief matching framework official implementation☆38Updated last year