Explainable machine learning in image classification models: An uncertainty quantification perspective
X Zhang, FTS Chan, S Mahadevan - Knowledge-Based Systems, 2022 - Elsevier
The poor explainability of deep learning models has hindered their adoption in safety and
quality-critical applications. This paper focuses on image classification models and aims to …
quality-critical applications. This paper focuses on image classification models and aims to …
On the calibration of multiclass classification with rejection
C Ni, N Charoenphakdee, J Honda… - Advances in Neural …, 2019 - proceedings.neurips.cc
We investigate the problem of multiclass classification with rejection, where a classifier can
choose not to make a prediction to avoid critical misclassification. First, we consider an …
choose not to make a prediction to avoid critical misclassification. First, we consider an …
[HTML][HTML] Deep learning uncertainty quantification for clinical text classification
Introduction: Machine learning algorithms are expected to work side-by-side with humans in
decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of …
decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of …
A multimodal movie review corpus for fine-grained opinion mining
In this paper, we introduce a set of opinion annotations for the POM movie review dataset,
composed of 1000 videos. The annotation campaign is motivated by the development of a …
composed of 1000 videos. The annotation campaign is motivated by the development of a …
Un cadre flexible pour l'apprentissage automatique interprétable: application à la classification d'images et d'audio
J Parekh - 2023 - theses.hal.science
Les systèmes d'apprentissage automatique, et en particulier les réseaux de neurones, ont
rapidement développé leur capacité à résoudre des problèmes d'apprentissage complexes …
rapidement développé leur capacité à résoudre des problèmes d'apprentissage complexes …