What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Machine learning, Information retrieval and Data mining.
Patrick Gallinari has researched Artificial intelligence in several fields, including Field and Social network.
His studies deal with areas such as Artificial neural network, Speech recognition and Feature as well as Pattern recognition.
The various areas that Patrick Gallinari examines in his Artificial neural network study include Algorithm and Feature selection.
His biological study spans a wide range of topics, including Categorization, XML, Structured document, Bayesian network and Generative model.
His work carried out in the field of Data mining brings together such families of science as Optimization problem, Recommender system, Benchmark and Generalization.
His most cited work include:
- The Wikipedia XML corpus (357 citations)
- SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent (292 citations)
- An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition (232 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Machine learning, Information retrieval, Artificial neural network and Pattern recognition are his primary areas of study.
His Artificial intelligence research is multidisciplinary, relying on both Speech recognition, Data mining and Natural language processing.
His Data mining research integrates issues from Recommender system and Graph.
Patrick Gallinari regularly links together related areas like Inference in his Machine learning studies.
While the research belongs to areas of Information retrieval, he spends his time largely on the problem of Document Structure Description, intersecting his research to questions surrounding XML validation.
His study focuses on the intersection of Artificial neural network and fields such as Set with connections in the field of Layer.
He most often published in these fields:
- Artificial intelligence (57.89%)
- Machine learning (28.16%)
- Information retrieval (19.21%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (57.89%)
- Natural language processing (12.63%)
- Machine learning (28.16%)
In recent papers he was focusing on the following fields of study:
Patrick Gallinari mostly deals with Artificial intelligence, Natural language processing, Machine learning, Deep learning and Artificial neural network.
Many of his research projects under Artificial intelligence are closely connected to Space with Space, tying the diverse disciplines of science together.
His Natural language processing research includes elements of Semantics and Word.
His research in Machine learning intersects with topics in Automatic identification and data capture, Metric, Partial differential equation, Inference and Domain adaptation.
His Deep learning study combines topics in areas such as Sea surface temperature, Remote sensing and Mesoscale meteorology.
His Artificial neural network research is multidisciplinary, incorporating elements of Dynamical system, Statistical learning theory and Spatial relation.
Between 2017 and 2021, his most popular works were:
- Deep learning for physical processes: incorporating prior scientific knowledge* (42 citations)
- Learning Dynamical Systems from Partial Observations. (32 citations)
- Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge (28 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Machine learning, Deep learning and Theoretical computer science.
Patrick Gallinari undertakes multidisciplinary studies into Artificial intelligence and Space in his work.
His study in Natural language processing is interdisciplinary in nature, drawing from both Semantics, Sequence, Structure and Hierarchical database model.
His work on Errors-in-variables models as part of general Machine learning study is frequently connected to Cardiac electrophysiology, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His work deals with themes such as Variety, Field and State, which intersect with Deep learning.
Patrick Gallinari combines subjects such as Domain, Inference and Set with his study of Theoretical computer science.
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