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Found 6 papers, 2 papers with code
T-Rep: Representation Learning for Time Series using Time-Embeddings
Multivariate time series present challenges to standard machine learning techniques, as they are often unlabeled, high dimensional, noisy, and contain missing data.
Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification
As a result, there is a widespread agreement on the importance of endowing Deep Learning models with explanatory capabilities so that they can themselves provide an answer to why a particular prediction was made.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications
Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs).
BIG-bench Machine Learning
Explainable artificial intelligence
+2
Should artificial agents ask for help in human-robot collaborative problem-solving?
Transferring as fast as possible the functioning of our brain to artificial intelligence is an ambitious goal that would help advance the state of the art in AI and robotics.
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Towards Explainable Neural-Symbolic Visual Reasoning
Many high-performance models suffer from a lack of interpretability.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
