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Found 10 papers, 5 papers with code
Concept-based Explainable Artificial Intelligence: A Survey
The field of explainable artificial intelligence emerged in response to the growing need for more transparent and reliable models.
Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning
Exploring exoplanets has transformed our understanding of the universe by revealing many planetary systems that defy our current understanding.
Explaining Speech Classification Models via Word-Level Audio Segments and Paralinguistic Features
Existing work focuses on a few spoken language understanding (SLU) tasks, and explanations are difficult to interpret for most users.
ITALIC: An Italian Intent Classification Dataset
Recent large-scale Spoken Language Understanding datasets focus predominantly on English and do not account for language-specific phenomena such as particular phonemes or words in different lects.
Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists
EAR also reveals overfitting terms, i. e., terms most likely to induce bias, to help identify their effect on the model, task, and predictions.
Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework.
Identifying Biased Subgroups in Ranking and Classification
Furthermore, we quantify the contribution of all attributes in the data subgroup to the divergent behavior by means of Shapley values, thus allowing the identification of the most impacting attributes.
Automating concept-drift detection by self-evaluating predictive model degradation
To this aim, suitable automatic solutions to self-assess the prediction quality and the data distribution drift between the original training set and the new data have to be devised.
Detecting Anomalies in Image Classification by Means of Semantic Relationships
This paper presents a semantic anomaly detection method (SAD) to detect anomalies in the predictions of any pixelwise semantic segmentation algorithm.
Scaling associative classification for very large datasets
DAC exploits ensemble learning to distribute the training of an associative classifier among parallel workers and improve the final quality of the model.
