What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Artificial intelligence, Case-based reasoning, Adaptation, Machine learning and World Wide Web.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Theoretical computer science, Creativity, Constraint and Natural language processing.
His Case-based reasoning study incorporates themes from Qualitative reasoning, Adaptive reasoning and Reasoning system.
His Machine learning research is multidisciplinary, incorporating elements of Model-based reasoning and Competence.
His studies examine the connections between World Wide Web and genetics, as well as such issues in User studies, with regards to Information retrieval.
His research integrates issues of Visual perception, Speech perception, Perception, Language production and Social cognition in his study of Analogy.
His most cited work include:
- Cognitive Psychology: A Student's Handbook (828 citations)
- Retrieval, reuse, revision and retention in case-based reasoning (458 citations)
- Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems (239 citations)
What are the main themes of his work throughout his whole career to date?
Mark T. Keane mostly deals with Artificial intelligence, Cognitive psychology, Cognitive science, Analogy and Case-based reasoning.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Natural language processing.
His work in Cognitive psychology covers topics such as Event which are related to areas like Outcome.
The study incorporates disciplines such as Argument, Computational model and Metaphor in addition to Analogy.
His biological study spans a wide range of topics, including Artificial neural network, Model-based reasoning, Reasoning system and Adaptation.
His Adaptation study frequently links to adjacent areas such as Context.
He most often published in these fields:
- Artificial intelligence (27.39%)
- Cognitive psychology (17.83%)
- Cognitive science (12.61%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (27.39%)
- Cognitive psychology (17.83%)
- Deep learning (3.91%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Cognitive psychology, Deep learning, Counterfactual conditional and Counterfactual thinking.
His study on Case-based reasoning is often connected to Post hoc as part of broader study in Artificial intelligence.
His work in Case-based reasoning addresses issues such as Recommender system, which are connected to fields such as Feature, Joint and Domain.
His work deals with themes such as Visual perception, Speech perception, Surprise and Statistical hypothesis testing, which intersect with Cognitive psychology.
His work focuses on many connections between Deep learning and other disciplines, such as Proxy, that overlap with his field of interest in Black box, Value, Uninterpretable and Test.
Mark T. Keane combines subjects such as Mathematical economics, Series, Econometrics and Competence with his study of Counterfactual thinking.
Between 2017 and 2021, his most popular works were:
- Attention to news and its dissemination on Twitter: A survey (18 citations)
- Twin-Systems to Explain Artificial Neural Networks using Case-Based Reasoning: Comparative Tests of Feature-Weighting Methods in ANN-CBR Twins for XAI (13 citations)
- Twin-Systems to Explain Artificial Neural Networks using Case-Based Reasoning: Comparative Tests of Feature-Weighting Methods in ANN-CBR Twins for XAI (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Mark T. Keane spends much of his time researching Counterfactual conditional, Artificial intelligence, Artificial neural network, Case-based reasoning and Counterfactual thinking.
Within one scientific family, he focuses on topics pertaining to Deep learning under Counterfactual conditional, and may sometimes address concerns connected to Interpretation.
His Artificial neural network study combines topics from a wide range of disciplines, such as Feature, Recommender system and Joint.
His work carried out in the field of Counterfactual thinking brings together such families of science as Mathematical economics, Econometrics and Competence.
His Cognitive psychology research extends to Competence, which is thematically connected.
His Cognitive psychology research incorporates elements of Phenomenon, Perception, Expression and Affect.
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