1 citations · 2 across the 5 of their papers we have counts for
5 papers
EMoE: Training-Free Expert Disagreement for Uncertainty-Aware Text-to-Image Diffusion
Lucas Berry, Axel Brando, Wei-Di Chang +2
Large text-to-image diffusion models rarely expose reliable signals of when a prompt is likely to produce a poorly aligned generation, especially when training data is undisclosed.…
Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models
Lucas Berry, Axel Brando, David Meger
Generative diffusion models, notable for their large parameter count (exceeding 100 million) and operation within high-dimensional image spaces, pose significant challenges for tra…
Uncertainty-aware hybrid paradigm of nonlinear MPC and model-based RL for offroad navigation: Exploration of transformers in the predictive model
Faraz Lotfi, Khalil Virji, Farnoosh Faraji +4
In this paper, we investigate a hybrid scheme that combines nonlinear model predictive control (MPC) and model-based reinforcement learning (RL) for navigation planning of an auton…
Efficient Epistemic Uncertainty Estimation in Regression Ensemble Models Using Pairwise-Distance Estimators
Lucas Berry, David Meger
This work introduces an efficient novel approach for epistemic uncertainty estimation for ensemble models for regression tasks using pairwise-distance estimators (PaiDEs). Utilizin…
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling
Lucas Berry, David Meger
In this work, we demonstrate how to reliably estimate epistemic uncertainty while maintaining the flexibility needed to capture complicated aleatoric distributions. To this end, we…