What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
His main research concerns Artificial intelligence, Artificial neural network, Theoretical computer science, Graph theory and Information retrieval.
His studies deal with areas such as Machine learning, Maxima and minima and Pattern recognition as well as Artificial intelligence.
His Artificial neural network study typically links adjacent topics like Learning environment.
His Graph theory research incorporates elements of Graph and Graph.
His work deals with themes such as Supervised learning and Directed acyclic graph, which intersect with Graph.
Marco Gori interconnects Web page, Data mining and Data set in the investigation of issues within Information retrieval.
His most cited work include:
- The Graph Neural Network Model (1992 citations)
- A new model for learning in graph domains (651 citations)
- Focused Crawling Using Context Graphs (598 citations)
What are the main themes of his work throughout his whole career to date?
Marco Gori mostly deals with Artificial intelligence, Artificial neural network, Machine learning, Theoretical computer science and Deep learning.
His biological study spans a wide range of topics, including Natural language processing and Pattern recognition.
His Artificial neural network research incorporates themes from Directed acyclic graph, Directed graph and Graph.
The concepts of his Theoretical computer science study are interwoven with issues in Graph theory and Graph.
His work in Deep learning addresses subjects such as Inference, which are connected to disciplines such as Probabilistic logic.
His Backpropagation study combines topics in areas such as Algorithm, Mathematical optimization and Maxima and minima.
He most often published in these fields:
- Artificial intelligence (58.33%)
- Artificial neural network (27.69%)
- Machine learning (17.74%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (58.33%)
- Deep learning (12.37%)
- Artificial neural network (27.69%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Deep learning, Artificial neural network, Theoretical computer science and Inference.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Scheme and Machine learning.
His studies in Deep learning integrate themes in fields like Training set, Convergence, Field, Crowdsourcing and Probabilistic logic.
His work in the fields of Backpropagation overlaps with other areas such as Action.
His biological study spans a wide range of topics, including Computational linguistics, Doors, Scheme, Set and Algebraic number.
The various areas that Marco Gori examines in his Inference study include Graphical model, Feature, Optimization problem and Model checking.
Between 2017 and 2021, his most popular works were:
- Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (37 citations)
- Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning (16 citations)
- Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
His primary areas of investigation include Artificial intelligence, Deep learning, Inference, Artificial neural network and Machine learning.
His work deals with themes such as Scheme and Cognition, which intersect with Artificial intelligence.
His Deep learning study integrates concerns from other disciplines, such as Theoretical computer science, Training set, Natural language processing, Contextual image classification and Cognitive science.
His Theoretical computer science research includes themes of Fragment, Quadratic programming and Kernel.
The Artificial neural network study combines topics in areas such as Initialization and Feature extraction.
In Supervised learning, Marco Gori works on issues like Graph, which are connected to Logical reasoning.
This overview was generated by a machine learning system which analysed the scientist’s
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