aamini / evidential-deep-learningLinks
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
☆508Updated 4 years ago
Alternatives and similar repositories for evidential-deep-learning
Users that are interested in evidential-deep-learning are comparing it to the libraries listed below
Sorting:
- Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertaint…☆639Updated 3 years ago
- This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"☆512Updated 2 years ago
- Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"☆275Updated 3 years ago
- High-quality implementations of standard and SOTA methods on a variety of tasks.☆1,565Updated last week
- Open-source framework for uncertainty and deep learning models in PyTorch☆476Updated this week
- ☆239Updated 5 years ago
- Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"☆478Updated 2 years ago
- The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a …☆372Updated 3 weeks ago
- A simple and extensible library to create Bayesian Neural Network layers on PyTorch.☆978Updated 2 years ago
- This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning mo…☆783Updated 2 months ago
- ☆110Updated 4 years ago
- Laplace approximations for Deep Learning.☆534Updated 9 months ago
- Reliability diagrams visualize whether a classifier model needs calibration☆165Updated 4 years ago
- Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".☆114Updated 3 years ago
- PyTorch implementation of bayesian neural network [torchbnn]☆553Updated last year
- This repository is the code for Predictive Uncertainty Estimation using Deep Ensemble☆157Updated 3 years ago
- Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty☆147Updated 2 years ago
- An implementation of the state-of-the-art Deep Active Learning algorithms☆107Updated 2 years ago
- Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)☆207Updated 4 years ago
- ☆472Updated last week
- This repository provides the code used to implement the framework to provide deep learning models with total uncertainty estimates as des…☆230Updated last year
- Domain adaptation made easy. Fully featured, modular, and customizable.☆392Updated 3 years ago
- Awesome Domain Adaptation Python Toolbox☆363Updated 2 months ago
- Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"☆581Updated 3 years ago
- Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true cla…☆255Updated 3 years ago
- Official Implementation of "Transformers Can Do Bayesian Inference", the PFN paper☆252Updated last year
- 👽 Out-of-Distribution Detection with PyTorch☆334Updated 3 weeks ago
- Implementation of Deep evidential regression paper☆58Updated 5 years ago
- A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.☆592Updated 2 years ago
- A simple way to calibrate your neural network.☆1,169Updated 6 months ago