EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media

23 May 2025

Papers citing "EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media"

7 / 7 papers shown

Title
A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications Siyuan Mu Sen Lin MoE 428 5 0 10 Mar 2025
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts Xiaoming Shi Shiyu Wang Yuqi Nie Dianqi Li Zhou Ye Qingsong Wen Ming Jin AI4TS 93 46 0 24 Sep 2024
Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users Laurent Valentin Jospin Wray Buntine F. Boussaïd Hamid Laga Bennamoun OOD BDL UQCV 79 627 0 14 Jul 2020
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods Eyke Hüllermeier Willem Waegeman PER UD 222 1,410 0 21 Oct 2019
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift Yaniv Ovadia Emily Fertig Jie Jessie Ren Zachary Nado D. Sculley Sebastian Nowozin Joshua V. Dillon Balaji Lakshminarayanan Jasper Snoek UQCV 159 1,691 0 06 Jun 2019
Evidential Deep Learning to Quantify Classification Uncertainty Murat Sensoy Lance M. Kaplan M. Kandemir OOD UQCV EDL BDL 177 991 0 05 Jun 2018
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Alex Kendall Y. Gal BDL OOD UD UQCV PER 352 4,704 0 15 Mar 2017