What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Tomaso Poggio focuses on Artificial intelligence, Computer vision, Cognitive neuroscience of visual object recognition, Pattern recognition and Neuroscience.
His Artificial intelligence study often links to related topics such as Machine learning.
As a part of the same scientific study, Tomaso Poggio usually deals with the Computer vision, concentrating on Pattern recognition and frequently concerns with Face and Set.
Tomaso Poggio has researched Cognitive neuroscience of visual object recognition in several fields, including Communication, Image processing, Representation, Form perception and Visual cortex.
Within one scientific family, Tomaso Poggio focuses on topics pertaining to Categorization under Neuroscience, and may sometimes address concerns connected to Stimulus.
His Artificial neural network research includes elements of Regularization, Theoretical computer science, Visual perception and Gaussian.
His most cited work include:
- Networks for approximation and learning (2941 citations)
- Hierarchical models of object recognition in cortex. (2763 citations)
- Face recognition: features versus templates (2268 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Cognitive neuroscience of visual object recognition and Visual cortex.
His Artificial intelligence study frequently intersects with other fields, such as Machine learning.
The study incorporates disciplines such as Robustness and Pattern recognition in addition to Computer vision.
His studies deal with areas such as Face, Face detection and Invariant as well as Pattern recognition.
His Cognitive neuroscience of visual object recognition research includes themes of Visual perception, Form perception, Temporal cortex and Categorization.
He most often published in these fields:
- Artificial intelligence (58.46%)
- Computer vision (24.92%)
- Pattern recognition (22.15%)
What were the highlights of his more recent work (between 2012-2021)?
- Artificial intelligence (58.46%)
- Pattern recognition (22.15%)
- Deep learning (4.15%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Pattern recognition, Deep learning, Invariant and Artificial neural network.
His biological study spans a wide range of topics, including Machine learning, Visual cortex and Computer vision.
His work in Pattern recognition tackles topics such as Set which are related to areas like Fusiform face area.
His Deep learning research incorporates themes from Theoretical computer science, Curse of dimensionality, Maxima and minima, Applied mathematics and Approximation theory.
In his research on the topic of Invariant, Algorithm is strongly related with Invariant.
Tomaso Poggio usually deals with Cognitive neuroscience of visual object recognition and limits it to topics linked to Facial recognition system and Cognitive science and Property.
Between 2012 and 2021, his most popular works were:
- Holographic embeddings of knowledge graphs (550 citations)
- Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review (250 citations)
- Deep vs. shallow networks: An approximation theory perspective (206 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Tomaso Poggio mainly investigates Artificial intelligence, Invariant, Pattern recognition, Cognitive neuroscience of visual object recognition and Theoretical computer science.
Tomaso Poggio has included themes like Machine learning and Invariant in his Artificial intelligence study.
His Invariant study integrates concerns from other disciplines, such as Pooling, Unsupervised learning, Neural decoding, Supervised learning and Topology.
Tomaso Poggio combines subjects such as Facial recognition system and Set with his study of Pattern recognition.
His research in Cognitive neuroscience of visual object recognition intersects with topics in Binocular neurons, Neuroscience, Visual cortex, Visual system and Motion perception.
His Theoretical computer science study incorporates themes from Structure, Operator, Content-addressable memory and Knowledge graph.
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