Attention Uncertainty

Attention uncertainty, the quantification of confidence in attention mechanisms within deep learning models, is a growing research area aiming to improve the reliability and robustness of these models. Current work focuses on incorporating uncertainty estimation into various architectures, including recurrent neural networks and generative adversarial networks, often leveraging techniques like Bayesian methods and attention refinement modules to address issues like missing data imputation and improved segmentation accuracy in challenging tasks such as medical image analysis and remote sensing. This research is significant because accurately representing and managing attention uncertainty leads to more reliable predictions and better understanding of model limitations, ultimately improving the trustworthiness and applicability of AI systems across diverse fields.

Papers

June 19, 2024

Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective
Meizhi Zhong, Chen Zhang, Yikun Lei, Xikai Liu, Yan Gao, Yao Hu, Kehai Chen, Min Zhang
Long Text Attention Pattern Long Context LLM Attention Uncertainty

January 4, 2024

Uncertainty-Aware Deep Attention Recurrent Neural Network for Heterogeneous Time Series Imputation
Linglong Qian, Zina Ibrahim, Richard Dobson
Imputation Algorithm Time Series Imputation Deep Imputation Imputation Task Attention Uncertainty

March 15, 2023

Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism
Han Yang, Qiuli Wang, Yue Zhang, Zhulin An, Chen Liu, Xiaohong Zhang, S. Kevin Zhou
Segmentation Annotation Segmentation Uncertainty Poor Segmentation Lung Nodule Segmentation Attention Uncertainty Uncertain Region

July 20, 2022

Exploiting Domain Transferability for Collaborative Inter-level Domain Adaptive Object Detection
Mirae Do, Seogkyu Jeon, Pilhyeon Lee, Kibeom Hong, Yu-seung Ma, Hyeran Byun
Domain Adaptation Domain Adaptive Object Detection Region Proposal Object Detection Pipeline Domain Transferability Attention Uncertainty

December 10, 2021

Uncertainty, Edge, and Reverse-Attention Guided Generative Adversarial Network for Automatic Building Detection in Remotely Sensed Images
Somrita Chattopadhyay, Avinash C. Kak
Generative Adversarial Network Semantic Segmentation High Uncertainty Anticipation Extreme Edge Building Detection Edge Attention Attention Uncertainty

Attention Uncertainty

Papers

Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective

Uncertainty-Aware Deep Attention Recurrent Neural Network for Heterogeneous Time Series Imputation

Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism

Exploiting Domain Transferability for Collaborative Inter-level Domain Adaptive Object Detection

Uncertainty, Edge, and Reverse-Attention Guided Generative Adversarial Network for Automatic Building Detection in Remotely Sensed Images