Papers Error Understanding
“Error Understanding” 태그가 달린 논문 9편 · 필터 해제
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors
While machine translation (MT) systems are achieving increasingly strong performance on benchmarks, they often produce translations with errors and anomalies. Understanding these errors can potentially help improve the t…
Error UnderstandingLanguage ModelingLanguage ModellingLarge Language Model+2Variable importance measure for spatial machine learning models with application to air pollution exposure prediction
Exposure assessment is fundamental to air pollution cohort studies. The objective is to predict air pollution exposures for study subjects at locations without data in order to optimize our ability to learn about health …
Error UnderstandingLess is More: Fewer Interpretable Region via Submodular Subset Selection
Image attribution algorithms aim to identify important regions that are highly relevant to model decisions. Although existing attribution solutions can effectively assign importance to target elements, they still face th…
Error UnderstandingImage AttributionInterpretability Techniques for Deep LearningError Detection in Egocentric Procedural Task Videos
We present a new egocentric procedural error dataset containing videos with various types of errors as well as normal videos and propose a new framework for procedural error detection using error-free training videos…
Action SegmentationActive Object DetectionAnomaly DetectionError Understanding+3Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure
This paper presents a new efficient black-box attribution method based on Hilbert-Schmidt Independence Criterion (HSIC), a dependence measure based on Reproducing Kernel Hilbert Spaces (RKHS). HSIC measures the dependenc…
Error UnderstandingImage AttributionInterpretability Techniques for Deep Learningobject-detection+1iSEA: An Interactive Pipeline for Semantic Error Analysis of NLP Models
Error analysis in NLP models is essential to successful model development and deployment. One common approach for diagnosing errors is to identify subpopulations in the dataset where the model produces the most errors. H…
Error UnderstandingScore-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions. In this paper, we develop a novel post-hoc visual explan…
Adversarial AttackDecision MakingError UnderstandingFairnessGrad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems. However, these deep models are perceived as "black box" methods considering the lack of unde…
3D Action RecognitionAction RecognitionCaption GenerationError Understanding+3Understanding Humans' Strategies in Maze Solving
Navigating through a visual maze relies on the strategic use of eye movements to select and identify the route. When navigating the maze, there are trade-offs between exploring to the environment and relying on memory. T…
Error Understanding