What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
His scientific interests lie mostly in Artificial intelligence, Machine learning, Neuroscience, Connectionism and Computational model.
His Artificial intelligence research is multidisciplinary, incorporating elements of Basis, Causal model and Set.
His study in the fields of Artificial neural network, Genetic representation and Interactive evolutionary computation under the domain of Machine learning overlaps with other disciplines such as Information system.
His study in the field of Inhibitory postsynaptic potential, Schizophrenic Psychology and Frontal lobe also crosses realms of Temporal lobe.
His Connectionism research integrates issues from Receptive field, Cortical map, Information processing, Mathematical optimization and Catastrophic interference.
His work focuses on many connections between Computational model and other disciplines, such as Cellular automaton, that overlap with his field of interest in Phenomenon.
His most cited work include:
- Abductive Inference Models for Diagnostic Problem-Solving (383 citations)
- Diagnostic Expert Systems Based on a Set Covering Model (359 citations)
- A Probabilistic Causal Model for Diagnostic Problem Solving Part I: Integrating Symbolic Causal Inference with Numeric Probabilistic Inference (225 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Artificial neural network, Neuroscience, Machine learning and Cognition.
His Artificial intelligence study combines topics in areas such as Process and Natural language processing.
While the research belongs to areas of Natural language processing, James A. Reggia spends his time largely on the problem of Speech recognition, intersecting his research to questions surrounding Set.
His research in the fields of Competitive learning overlaps with other disciplines such as Naive Bayes classifier.
His Cognition study combines topics from a wide range of disciplines, such as Cognitive psychology and Cognitive science.
His Inference research incorporates elements of Diagnostic reasoning and Model-based reasoning, Knowledge representation and reasoning.
He most often published in these fields:
- Artificial intelligence (54.61%)
- Artificial neural network (18.77%)
- Neuroscience (16.72%)
What were the highlights of his more recent work (between 2008-2021)?
- Artificial intelligence (54.61%)
- Artificial neural network (18.77%)
- Machine learning (18.09%)
In recent papers he was focusing on the following fields of study:
James A. Reggia mainly focuses on Artificial intelligence, Artificial neural network, Machine learning, Cognition and Working memory.
His studies in Artificial intelligence integrate themes in fields like Swarm intelligence, Structure and Natural language processing.
His Artificial neural network research is multidisciplinary, relying on both Network architecture, Data mining and Encoding.
His study in the field of Self-organizing map is also linked to topics like Left behind.
In his study, Cognitive science, Phenomenology and Artificial general intelligence is strongly linked to Consciousness, which falls under the umbrella field of Cognition.
His research integrates issues of Attractor, Task and Hebbian theory in his study of Working memory.
Between 2008 and 2021, his most popular works were:
- The rise of machine consciousness: Studying consciousness with computational models (78 citations)
- A generalized LSTM-like training algorithm for second-order recurrent neural networks (50 citations)
- Causally-guided evolutionary optimization and its application to antenna array design (45 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
James A. Reggia mostly deals with Artificial intelligence, Artificial neural network, Machine learning, Cognition and Algorithm.
His work carried out in the field of Artificial intelligence brings together such families of science as Swarm intelligence, Set and Process.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Memoria, Ant colony optimization algorithms and Forgetting.
He interconnects Network architecture, Locality, Locality of reference and Knowledge-based systems in the investigation of issues within Machine learning.
The various areas that James A. Reggia examines in his Cognition study include Consciousness and Human intelligence.
His research in Algorithm intersects with topics in Hebbian theory, Memory span, Short-term memory, Models of neural computation and Neural substrate.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
