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