High Uncertainty Anticipation

High uncertainty anticipation focuses on developing methods to accurately quantify and manage uncertainty in model predictions across diverse fields, aiming to improve the reliability and trustworthiness of AI systems. Current research emphasizes integrating uncertainty estimation into various model architectures, including neural networks, diffusion models, and graph neural networks, often employing techniques like Bayesian methods, conformal prediction, and ensemble methods. This work is crucial for deploying AI in high-stakes applications like healthcare, autonomous driving, and finance, where reliable uncertainty quantification is paramount for safe and effective decision-making.

Papers

December 1, 2022

An introduction to optimization under uncertainty -- A short survey
Keivan Shariatmadar, Kaizheng Wang, Calvin R. Hubbard, Hans Hallez, David Moens
Optimization Purpose High Uncertainty Anticipation Epistemic Uncertainty Gentle Introduction Robust Solution

November 28, 2022

What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems
Gilad Kutiel, Regev Cohen, Michael Elad, Daniel Freedman
High Uncertainty Anticipation Image to Image Mask Frozen DETR Image Completion Image to Image Translation Problem

November 26, 2022

Looking at the posterior: accuracy and uncertainty of neural-network predictions
H. Linander, O. Balabanov, H. Yang, B. Mehlig
Uncertainty Quantification High Uncertainty Anticipation Bayesian Inference Aleatoric Uncertainty Uncertainty Estimation Method Neural Network Prediction

November 17, 2022

Introduction and Exemplars of Uncertainty Decomposition
Shuo Chen
Gaussian Process High Uncertainty Anticipation Ensemble Learning Gentle Introduction Exemplar Image Uncertainty Decomposition

November 14, 2022

Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition
Jean-Rémy Conti, Stéphan Clémençon
Procedural Fairness System Performance High Uncertainty Anticipation Face Recognition Area Under the ROC Curve ROC Curve Similarity Assessment

November 11, 2022

Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression
Levente Foldesi, Matias Valdenegro-Toro
Deep Learning Human Prediction Time Series Uncertainty Quantification High Uncertainty Anticipation Consistent Comparison Uncertainty Estimation Uncertainty Estimation Method

October 30, 2022

A view on model misspecification in uncertainty quantification
Yuko Kato, David M. J. Tax, Marco Loog
Machine Learning Model Human Prediction Uncertainty Quantification High Uncertainty Anticipation Uncertainty Estimation Best View Model Misspecification

October 26, 2022

Can Transformer Attention Spread Give Insights Into Uncertainty of Detected and Tracked Objects?
Felicia Ruppel, Florian Faion, Claudius Gläser, Klaus Dietmayer
High Uncertainty Anticipation DCU Insight AQ Attention Weight Attention Matrix Transformer Layer Transformer Attention Detection of Uncertainty in Exceedance

October 21, 2022

Considerations for Visualizing Uncertainty in Clinical Machine Learning Models
Caitlin F. Harrigan, Gabriela Morgenshtern, Anna Goldenberg, Fanny Chevalier
High Uncertainty Anticipation Predictive Model Interpretable Feature Crucial Consideration Actionable Part Clinical Machine Learning

October 18, 2022

October 17, 2022

On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning
Philipp Becker, Gerhard Neumann
High Uncertainty Anticipation State Space Model Model Based Reinforcement Learning Deep State Space Model Recurrent State Space Model

October 7, 2022

IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty
Gwangbin Bae, Ignas Budvytis, Roberto Cipolla
High Uncertainty Anticipation Depth Estimation Iterative Refinement Depth Completion Surface Normal Depth Refinement Single View Depth

October 5, 2022

September 30, 2022

September 29, 2022

Quantifying Harm
Sander Beckers, Hana Chockler, Joseph Y. Halpern
High Uncertainty Anticipation Potential Harm Qualitative Reasoning

September 27, 2022

BayesNetCNN: incorporating uncertainty in neural networks for image-based classification tasks
Matteo Ferrante, Tommaso Boccato, Nicola Toschi
Neural Network High Uncertainty Anticipation Bayesian Neural Network Image Classification Task Based Rejection Bayes Net Intrinsic Uncertainty

High Uncertainty Anticipation

Papers

An introduction to optimization under uncertainty -- A short survey

What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems

Looking at the posterior: accuracy and uncertainty of neural-network predictions

Introduction and Exemplars of Uncertainty Decomposition

Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition

Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression

A view on model misspecification in uncertainty quantification

Can Transformer Attention Spread Give Insights Into Uncertainty of Detected and Tracked Objects?

Considerations for Visualizing Uncertainty in Clinical Machine Learning Models

Planning with SiMBA: Motion Planning under Uncertainty for Temporal Goals using Simplified Belief Guides

Uncertainty in Extreme Multi-label Classification

On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning

IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty

Query The Agent: Improving sample efficiency through epistemic uncertainty estimation

Boundary-Aware Uncertainty for Feature Attribution Explainers

Prioritizing emergency evacuations under compounding levels of uncertainty

Bayesian Neural Networks for Geothermal Resource Assessment: Prediction with Uncertainty

A Multiple Criteria Decision Analysis based Approach to Remove Uncertainty in SMP Models

Quantifying Harm

BayesNetCNN: incorporating uncertainty in neural networks for image-based classification tasks