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.

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

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

Calibrating LLMs with Information-Theoretic Evidential Deep Learning (ICLR 2025)

Evidential Deep Learning Layers for Flux

Personalized segmentation adapts a pretrained model to institution-specific annotation standards using limited labeled data and uncertainty-guided sample selection.

Our Conflict-aware Evidential Deep Learning (C-EDL) method enhances robustness to OOD and adversarial inputs by combining evidence from metamorphic transformations and reducing evidence when conflicts arise, signalling higher uncertainty.