evidential-deep-learning

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

A curated publication list on evidential deep learning.

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

[ICCV 2021 Oral] Deep Evidential Action Recognition

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

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

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

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

[ICML 2026] Revisiting Uncertainty: On Evidential Learning for Partially Relevant Video Retrieval

[TPAMI 2025] Revisiting Essential and Non-Essential Settings of Evidential Deep Learning

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)

[NeurIPS 2024] Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?

Official Implementation of Evidential Knowledge Distillation (ICCV 2025)

Evidential Deep Learning Layers for Flux

Hybrid Uncertainty Quantification for Bioactivity Assessment

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.

Label-free confidence estimation for neural networks - benchmarking MSP, MC Dropout, EDL, and Deep Ensembles with a novel unsupervised uncertainty metric.

Multi-label Chest X-ray classification on ChestMNIST using LISA (Language-Invariant Semantic Anchoring) and PaQ (Pathology-as-Query) with conformal safety guarantees, evidential deep learning, and post-hoc calibration.