What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
The scientist’s investigation covers issues in Compressed sensing, Algorithm, Artificial intelligence, Computer vision and Signal processing.
His Compressed sensing research integrates issues from Signal reconstruction, Quantization, Sparse matrix, Signal and Iterative reconstruction.
His Algorithm research incorporates elements of Information theory, Speech recognition, Theoretical computer science and Mathematical optimization.
His work investigates the relationship between Artificial intelligence and topics such as Pattern recognition that intersect with problems in Greedy algorithm and Contextual image classification.
His Signal processing research also works with subjects such as
- Bandwidth, which have a strong connection to Oversampling and Wideband,
- Nyquist–Shannon sampling theorem which intersects with area such as Undersampling.
His Wavelet transform study deals with Image processing intersecting with Minification.
His most cited work include:
- Compressive Sensing [Lecture Notes] (2872 citations)
- Single-Pixel Imaging via Compressive Sampling (2362 citations)
- A Simple Proof of the Restricted Isometry Property for Random Matrices (2130 citations)
What are the main themes of his work throughout his whole career to date?
Richard G. Baraniuk focuses on Artificial intelligence, Algorithm, Compressed sensing, Wavelet and Computer vision.
His research in Artificial intelligence intersects with topics in Machine learning and Pattern recognition.
In his research, Time–frequency analysis is intimately related to Signal processing, which falls under the overarching field of Algorithm.
The various areas that Richard G. Baraniuk examines in his Compressed sensing study include Sampling, Signal, Signal reconstruction, Quantization and Electronic engineering.
His Wavelet study frequently draws connections between adjacent fields such as Mathematical analysis.
His study in Data compression and Image is carried out as part of his Computer vision studies.
He most often published in these fields:
- Artificial intelligence (34.40%)
- Algorithm (31.73%)
- Compressed sensing (23.20%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (34.40%)
- Algorithm (31.73%)
- Machine learning (8.53%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Algorithm, Machine learning, Compressed sensing and Spline.
The concepts of his Artificial intelligence study are interwoven with issues in Computer vision and Pattern recognition.
His study in Computer vision is interdisciplinary in nature, drawing from both Lens and Sampling.
Richard G. Baraniuk studies Computation which is a part of Algorithm.
The Machine learning study combines topics in areas such as Variety and Representation.
His Compressed sensing study incorporates themes from Signal processing, Convolutional neural network, Random projection and Convex optimization.
Between 2016 and 2021, his most popular works were:
- Learning to invert: Signal recovery via Deep Convolutional Networks (165 citations)
- Learned D-AMP: Principled! Neural network based compressive image recovery (134 citations)
- FlatCam: Thin, Lensless Cameras Using Coded Aperture and Computation (76 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Richard G. Baraniuk mostly deals with Algorithm, Artificial intelligence, Compressed sensing, Convolutional neural network and Deep learning.
His studies deal with areas such as Theoretical computer science, Training set, Tangent, Phase retrieval and Bandwidth as well as Algorithm.
The Artificial intelligence study combines topics in areas such as Machine learning, Key and Computer vision.
His Computer vision research incorporates themes from Sampling, Signal and Signal processing.
His work deals with themes such as Quantization, Thresholding, Convex optimization, Transformation and Pattern recognition, which intersect with Compressed sensing.
His Deep learning study combines topics in areas such as Overfitting, Metric, Spline, Cluster analysis and Wavelet.
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