What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
Compressed sensing, Artificial intelligence, Algorithm, Computer vision and Iterative reconstruction are his primary areas of study.
His work deals with themes such as Theoretical computer science, Sparse approximation, Signal processing, Computation and Wavelet, which intersect with Compressed sensing.
In his research, Time series and Matched filter is intimately related to Pattern recognition, which falls under the overarching field of Artificial intelligence.
Marco F. Duarte has included themes like Information theory and Speech recognition in his Algorithm study.
His studies in Computer vision integrate themes in fields like Optical computing and Digital micromirror device.
As a member of one scientific family, Marco F. Duarte mostly works in the field of Iterative reconstruction, focusing on Signal and, on occasion, Decoding methods.
His most cited work include:
- Single-Pixel Imaging via Compressive Sampling (2362 citations)
- Model-Based Compressive Sensing (1446 citations)
- Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals (851 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Compressed sensing, Artificial intelligence, Algorithm, Pattern recognition and Computer vision.
His Compressed sensing study combines topics from a wide range of disciplines, such as Theoretical computer science, Signal reconstruction, Sparse matrix, Sparse approximation and Signal.
His Artificial intelligence study frequently involves adjacent topics like Machine learning.
His Algorithm research includes elements of Bandlimiting, Nyquist–Shannon sampling theorem, Sampling, Nyquist rate and Mathematical optimization.
His research in Nyquist–Shannon sampling theorem intersects with topics in Analog signal, Greedy algorithm, Signal processing, Robustness and Restricted isometry property.
In his work, Power graph analysis, Graph, Feature vector and Feature selection is strongly intertwined with Autoencoder, which is a subfield of Pattern recognition.
He most often published in these fields:
- Compressed sensing (50.68%)
- Artificial intelligence (42.47%)
- Algorithm (40.41%)
What were the highlights of his more recent work (between 2016-2020)?
- Artificial intelligence (42.47%)
- Algorithm (40.41%)
- Pattern recognition (25.34%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Algorithm, Pattern recognition, Feature extraction and Training set.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Metric.
His research integrates issues of Principal component analysis and Wideband in his study of Algorithm.
The concepts of his Pattern recognition study are interwoven with issues in Autoencoder and Linear model.
The various areas that he examines in his Sparse approximation study include Leverage, Parameter space, Estimation theory, Compressed sensing and Euclidean distance.
Marco F. Duarte combines Compressed sensing and Convex optimization in his research.
Between 2016 and 2020, his most popular works were:
- Explainable Machine Learning for Scientific Insights and Discoveries (82 citations)
- Hyperspectral Band Selection From Statistical Wavelet Models (24 citations)
- Graph autoencoder-based unsupervised feature selection with broad and local data structure preservation (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Marco F. Duarte focuses on Artificial intelligence, Pattern recognition, Dimensionality reduction, Machine learning and Feature extraction.
Many of his studies involve connections with topics such as Metric and Artificial intelligence.
His work on Wavelet transform and Wavelet as part of his general Pattern recognition study is frequently connected to Redundancy, thereby bridging the divide between different branches of science.
The study incorporates disciplines such as Deep learning, Activity recognition, Relevance and Hyperspectral imaging in addition to Feature extraction.
His work in Training set tackles topics such as Compact space which are related to areas like Algorithm.
His work on Sparse approximation as part of general Algorithm research is often related to Extension, thus linking different fields of science.
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