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Found 6 papers, 0 papers with code
Active Learning for Imbalanced Civil Infrastructure Data
Unfortunately, annotation costs are incredibly high as our proprietary civil engineering dataset must be annotated by highly trained engineers.
Model-Assisted Labeling via Explainability for Visual Inspection of Civil Infrastructures
These are used within an assisted labeling framework where the annotators can interact with them as proposal segmentation masks by deciding to accept, reject or modify them, and interactions are logged as weak labels to further refine the classifier.
Attention-based Interpretability with Concept Transformers
In particular, we design the Concept Transformer, a deep learning module that exposes explanations of the output of a model in which it is embedded in terms of attention over user-defined high-level concepts.
From Time Series to Euclidean Spaces: On Spatial Transformations for Temporal Clustering
Unsupervised clustering of temporal data is both challenging and crucial in machine learning.
On Evaluating Explainability Algorithms
A plethora of methods attempting to explain predictions of black-box models have been proposed by the Explainable Artificial Intelligence (XAI) community.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Explainable Failure Predictions with RNN Classifiers based on Time Series Data
Given key performance indicators collected with fine granularity as time series, our aim is to predict and explain failures in storage environments.
