Search Results for author:
Found 9 papers, 3 papers with code
Towards Feasible Counterfactual Explanations: A Taxonomy Guided Template-based NLG Method
Counterfactual Explanations (cf-XAI) describe the smallest changes in feature values necessary to change an outcome from one class to another.
Behaviour Trees for Creating Conversational Explanation Experiences
Formalising the XAI system promotes the reuse of existing explainers and known explanation needs that can be refined and evolved over time using user evaluation feedback.
Clinical Dialogue Transcription Error Correction using Seq2Seq Models
This information contributes to medical decision-making regarding the patient and plays a crucial role in their healthcare journey.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
FedSim: Similarity guided model aggregation for Federated Learning
Effective aggregation of client models is essential to create a generalised global model.
DisCERN:Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods
For this purpose a counterfactual explainer needs to discover input dependencies that relate to outcome changes.
Learning-to-Learn Personalised Human Activity Recognition Models
We present a meta-learning methodology for learning to learn personalised HAR models for HAR; with the expectation that the end-user need only provides a few labelled data but can benefit from the rapid adaptation of a generic meta-model.
FitChat: Conversational Artificial Intelligence Interventions for Encouraging Physical Activity in Older Adults
For this reason, chatbots have recently been seen in healthcare delivering digital interventions through free text or choice selection.
MEx: Multi-modal Exercises Dataset for Human Activity Recognition
In addition we visualise pressure mat data to explore the potential of the sensor to capture exercise performance quality.
