What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Roderick Murray-Smith mostly deals with Human–computer interaction, Algorithm, Gaussian process, Artificial intelligence and Mobile device.
In general Human–computer interaction, his work in Multimodal interaction is often linked to Bandwidth linking many areas of study.
His Algorithm research is multidisciplinary, relying on both Probabilistic logic, Inference, State space and System identification.
His studies in Gaussian process integrate themes in fields like Identification, Point estimation, Statistics, Kriging and Applied mathematics.
His study on Artificial intelligence also encompasses disciplines like
- Computer vision that intertwine with fields like Interaction device and Deep learning,
- Machine learning which intersects with area such as Nonlinear system.
His Mobile device study combines topics in areas such as Embedded system and Audio feedback.
His most cited work include:
- Multiple Model Approaches to Modelling and Control (640 citations)
- Gaussian Process Priors with Uncertain Inputs Application to Multiple-Step Ahead Time Series Forecasting (285 citations)
- On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models (281 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Human–computer interaction, Mobile device, Gaussian process and Computer vision.
His Artificial intelligence research includes elements of Identification, Machine learning and Pattern recognition.
The study incorporates disciplines such as Multimedia, Brain–computer interface and Haptic technology in addition to Human–computer interaction.
In his study, which falls under the umbrella issue of Mobile device, Gesture is strongly linked to Simulation.
His Gaussian process research incorporates elements of Algorithm, Mathematical optimization, Nonparametric statistics and Nonlinear system.
His Algorithm research includes themes of Control theory, Statistical model and System identification.
He most often published in these fields:
- Artificial intelligence (40.25%)
- Human–computer interaction (28.39%)
- Mobile device (15.25%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (40.25%)
- Pattern recognition (7.20%)
- Computer vision (14.83%)
In recent papers he was focusing on the following fields of study:
Roderick Murray-Smith mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Deep learning and Detector.
Roderick Murray-Smith interconnects Lidar and Machine learning in the investigation of issues within Artificial intelligence.
Roderick Murray-Smith has researched Machine learning in several fields, including Image, Inference, State and Holography.
The various areas that Roderick Murray-Smith examines in his Pattern recognition study include Feature, Key and Benchmark.
His studies deal with areas such as Pixel, Digital image processing and Photon as well as Detector.
As part of one scientific family, Roderick Murray-Smith deals mainly with the area of Artificial neural network, narrowing it down to issues related to the Object, and often Cluster analysis, Identification, Tracking and Obstacle.
Between 2017 and 2021, his most popular works were:
- Deep learning for real-time single-pixel video. (79 citations)
- Practical classification of different moving targets using automotive radar and deep neural networks (37 citations)
- Transmission of natural scene images through a multimode fibre. (36 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Roderick Murray-Smith focuses on Artificial intelligence, Pattern recognition, Deep learning, Computer vision and Artificial neural network.
His Artificial intelligence study frequently draws connections between related disciplines such as Inverse problem.
The various areas that Roderick Murray-Smith examines in his Pattern recognition study include Tree traversal, Logarithm, Gradient descent and Benchmark.
Roderick Murray-Smith interconnects Unsupervised learning, Feature learning and Feature in the investigation of issues within Deep learning.
Roderick Murray-Smith has researched Computer vision in several fields, including Optical communication and Detector.
His Artificial neural network study combines topics from a wide range of disciplines, such as Convolutional neural network, Spectrogram, Video tracking, Radar tracker and Doppler radar.
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