What is he best known for?
The fields of study he is best known for:
- Machine learning
- Artificial intelligence
- Algorithm
Philip M. Long mainly investigates Discrete mathematics, Combinatorics, Algorithm, Genetics and DNA microarray.
His research in the fields of Constant factor overlaps with other disciplines such as Class.
The concepts of his Algorithm study are interwoven with issues in Supervised learning, Boosting, Mathematical optimization and AdaBoost.
His work on Basal-like carcinoma as part of general Genetics study is frequently linked to Basal-Like Breast Carcinoma, Estrogen receptor and Breast cancer classification, therefore connecting diverse disciplines of science.
His DNA microarray research includes themes of Microarray, Computational biology, Hepatocellular carcinoma and Pathology.
He combines subjects such as Hybridization probe, Complementary DNA, In silico and Genomics with his study of Gene expression.
His most cited work include:
- Breast cancer classification and prognosis based on gene expression profiles from a population-based study (1713 citations)
- Comparative full-length genome sequence analysis of 14 SARS coronavirus isolates and common mutations associated with putative origins of infection (417 citations)
- Comment on “ 'Stemness': Transcriptional Profiling of Embryonic and Adult Stem Cells” and “A Stem Cell Molecular Signature” (I) (304 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Discrete mathematics, Algorithm, Combinatorics and Machine learning are his primary areas of study.
The study of Artificial intelligence is intertwined with the study of Pattern recognition in a number of ways.
His Discrete mathematics study integrates concerns from other disciplines, such as Function, Computational learning theory and Constant.
His Constant study combines topics in areas such as Generalization and Lipschitz continuity.
In his work, Time complexity is strongly intertwined with Active learning, which is a subfield of Algorithm.
His study on Integer is often connected to Sample complexity as part of broader study in Combinatorics.
He most often published in these fields:
- Artificial intelligence (25.97%)
- Discrete mathematics (25.32%)
- Algorithm (25.32%)
What were the highlights of his more recent work (between 2017-2021)?
- Algorithm (25.32%)
- Gradient descent (8.44%)
- Residual (5.19%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Algorithm, Gradient descent, Residual, Distribution and Linear map.
Philip M. Long has researched Algorithm in several fields, including Generalization, Convolutional neural network, Lipschitz continuity and Constant.
His Gradient descent research incorporates elements of Positive-definite matrix, Margin, Linear separability and Applied mathematics.
His work focuses on many connections between Residual and other disciplines, such as Singular value, that overlap with his field of interest in Computation, Normalization, Least squares and Eigenvalues and eigenvectors.
His Distribution research integrates issues from Integer and Combinatorics.
In his study, Regularization and Quadratic equation is strongly linked to Invariant, which falls under the umbrella field of Combinatorics.
Between 2017 and 2021, his most popular works were:
- Benign overfitting in linear regression (112 citations)
- The Singular Values of Convolutional Layers (58 citations)
- The Singular Values of Convolutional Layers (45 citations)
In his most recent research, the most cited papers focused on:
- Machine learning
- Artificial intelligence
- Algorithm
Philip M. Long focuses on Algorithm, Linear map, Residual, Gradient descent and Initialization.
Philip M. Long works mostly in the field of Algorithm, limiting it down to topics relating to Dimension and, in certain cases, Statistical learning theory.
His Linear map study incorporates themes from Singular value, Normalization and Computation.
His Residual research includes elements of Identity function, Pure mathematics, Identity and Sigmoid function.
His studies deal with areas such as Linear separability, Margin, Linear classifier, Fraction and Limit as well as Gradient descent.
His Initialization study spans across into fields like Positive-definite matrix, Identity, Algebra, Function and Parameterized complexity.
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