This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"

[ICCV 2021 Oral] Deep Evidential Action Recognition

Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

[ECCV 2022] Dual-Evidential Learning for Weakly-supervised Temporal Action Localization

[ICLR 2024 Spotlight] R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning

Machine learning models for estimating aleatoric and epistemic uncertainty with evidential and ensemble methods.

Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022

Official implementation of MICCAI2024 paper "Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis"

Implementation of "Evidential Deep Learning to Quantify Classification Uncertainty" proposing a method to quantify uncertainty in a neural network.

Evidential Deep Learning Layers for Flux