What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Fabio Ramos mostly deals with Artificial intelligence, Computer vision, Gaussian process, Machine learning and Pattern recognition.
His work in Artificial intelligence addresses subjects such as Algorithm, which are connected to disciplines such as Mathematical optimization.
His research in the fields of Video tracking and Tracking overlaps with other disciplines such as Detector.
His research integrates issues of Class, Human–robot interaction and Bayesian probability in his study of Machine learning.
The Pattern recognition study combines topics in areas such as Cognitive neuroscience of visual object recognition and Relation.
His study in the field of Mobile robot is also linked to topics like Social robot.
His most cited work include:
- Simple online and realtime tracking (559 citations)
- Gaussian process occupancy maps (140 citations)
- Gaussian process modeling of large-scale terrain (128 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Machine learning, Computer vision, Gaussian process and Robot are his primary areas of study.
As a part of the same scientific family, Fabio Ramos mostly works in the field of Artificial intelligence, focusing on Pattern recognition and, on occasion, Representation.
Machine learning and Inference are frequently intertwined in his study.
The study incorporates disciplines such as Dimensionality reduction and Conditional random field in addition to Computer vision.
His work on Motion planning as part of general Robot research is frequently linked to Process, thereby connecting diverse disciplines of science.
His Probabilistic logic research incorporates themes from Statistical model and Bayesian inference.
He most often published in these fields:
- Artificial intelligence (56.65%)
- Machine learning (25.95%)
- Computer vision (21.20%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (56.65%)
- Machine learning (25.95%)
- Robot (20.25%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Machine learning, Robot, Algorithm and Bayesian probability.
Fabio Ramos integrates several fields in his works, including Artificial intelligence and Task analysis.
The various areas that he examines in his Machine learning study include Range and Computational model.
His Robot research incorporates elements of Real-time computing and Occupancy.
His Algorithm study combines topics from a wide range of disciplines, such as Stochastic process, Iterative closest point, Point cloud and Kernel.
His studies in Bayesian probability integrate themes in fields like Bayesian optimization, Mathematical optimization, Sampling, Probabilistic logic and Sensor fusion.
Between 2017 and 2021, his most popular works were:
- BayesSim: Adaptive Domain Randomization Via Probabilistic Inference for Robotics Simulators (48 citations)
- Malicious Software Classification Using VGG16 Deep Neural Network’s Bottleneck Features (15 citations)
- Models that learn how humans learn: The case of decision-making and its disorders. (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Artificial intelligence, Machine learning, Robot, Algorithm and Bayesian probability.
Borrowing concepts from Task analysis, he weaves in ideas under Artificial intelligence.
The various areas that Fabio Ramos examines in his Machine learning study include Class, Sample and Grayscale.
His study looks at the relationship between Robot and topics such as Real-time computing, which overlap with Bayesian optimization, Representation and Statistical model.
His biological study spans a wide range of topics, including Feature, Ideal, Stochastic process, Time series modelling and Fourier transform.
Fabio Ramos usually deals with Bayesian probability and limits it to topics linked to Probability distribution and Probability density function, Mixture distribution, Benchmark, Pattern recognition and Object.
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