What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Stephen J. Roberts focuses on Artificial intelligence, Pattern recognition, Bayesian probability, Data mining and Machine learning.
His work in the fields of Mixture model overlaps with other areas such as Computer Applications.
His work in the fields of Pattern recognition, such as Pattern recognition, intersects with other areas such as Computerized analysis.
His study in Bayesian probability is interdisciplinary in nature, drawing from both Node, Monte Carlo method, Inference and Model selection.
Stephen J. Roberts combines subjects such as Confusion matrix, Geodetic datum, Gaussian process, Probabilistic logic and Domain knowledge with his study of Data mining.
His work on Multilayer perceptron and Artificial neural network as part of his general Machine learning study is frequently connected to Manx shearwater and Puffinus, thereby bridging the divide between different branches of science.
His most cited work include:
- Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection (491 citations)
- Gaussian processes for time-series modelling (286 citations)
- Overlapping community detection using Bayesian non-negative matrix factorization. (280 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Machine learning, Gaussian process, Pattern recognition and Bayesian probability.
Artificial intelligence connects with themes related to Signal processing in his study.
His Machine learning study combines topics from a wide range of disciplines, such as Heuristics and Bayesian inference.
The concepts of his Gaussian process study are interwoven with issues in Algorithm, Mathematical optimization and Time series.
Stephen J. Roberts regularly ties together related areas like Autoregressive model in his Pattern recognition studies.
His work carried out in the field of Bayesian probability brings together such families of science as Data mining and Hyperparameter.
He most often published in these fields:
- Artificial intelligence (43.89%)
- Machine learning (23.05%)
- Gaussian process (16.63%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (43.89%)
- Machine learning (23.05%)
- Gaussian process (16.63%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Machine learning, Gaussian process, Mathematical optimization and Deep learning.
As part of the same scientific family, Stephen J. Roberts usually focuses on Artificial intelligence, concentrating on Pattern recognition and intersecting with Cluster analysis.
His research integrates issues of Probabilistic logic, Training set, Heuristics and Robustness in his study of Machine learning.
His study focuses on the intersection of Gaussian process and fields such as Algorithm with connections in the field of Estimator, Scale and Noise.
Stephen J. Roberts has researched Mathematical optimization in several fields, including Nonparametric statistics and Financial market.
His Deep learning research is multidisciplinary, relying on both Sharpe ratio, Stock exchange, Futures contract, Volatility and Convolutional neural network.
Between 2017 and 2021, his most popular works were:
- DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (51 citations)
- The surgical safety checklist and patient outcomes after surgery: a prospective observational cohort study, systematic review and meta-analysis (44 citations)
- Functional and informatics analysis enables glycosyltransferase activity prediction (29 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Machine learning, Inference, Mathematical optimization and Reinforcement learning.
His Artificial intelligence research incorporates elements of Adversary, Pattern recognition and Time series.
Stephen J. Roberts has included themes like Data modeling, Gaussian process and Bayesian probability in his Machine learning study.
His Gaussian process research is multidisciplinary, incorporating perspectives in Algorithm, Statistical physics, Task and Robustness.
His Bayesian probability research integrates issues from Principle of maximum entropy and Equivalence.
The study incorporates disciplines such as Prior probability, Artificial neural network, Kernel regression, Random forest and Dropout in addition to Inference.
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