What is he best known for?
The fields of study he is best known for:
- Machine learning
- Statistics
- Artificial intelligence
Quanquan Gu mostly deals with Artificial intelligence, Gradient descent, Algorithm, Pattern recognition and Feature selection.
The Artificial intelligence study combines topics in areas such as Optimization problem and Machine learning.
His Gradient descent study combines topics from a wide range of disciplines, such as Deep learning and Maxima and minima.
As a part of the same scientific study, Quanquan Gu usually deals with the Algorithm, concentrating on Upper and lower bounds and frequently concerns with Polynomial.
His research in Pattern recognition intersects with topics in Subspace topology and Biclustering.
Quanquan Gu interconnects Scoring algorithm and Integer programming in the investigation of issues within Feature selection.
His most cited work include:
- Personalized entity recommendation: a heterogeneous information network approach (362 citations)
- Generalized Fisher score for feature selection (210 citations)
- Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks (196 citations)
What are the main themes of his work throughout his whole career to date?
Quanquan Gu mainly focuses on Artificial intelligence, Algorithm, Gradient descent, Applied mathematics and Mathematical optimization.
The study incorporates disciplines such as Machine learning and Pattern recognition in addition to Artificial intelligence.
The various areas that Quanquan Gu examines in his Algorithm study include Artificial neural network, Upper and lower bounds and Generalization.
In his works, Quanquan Gu conducts interdisciplinary research on Gradient descent and Initialization.
His biological study spans a wide range of topics, including Sampling, Regularization, Estimator and Stochastic gradient descent.
His Mathematical optimization study integrates concerns from other disciplines, such as Robustness and Benchmark.
He most often published in these fields:
- Artificial intelligence (27.50%)
- Algorithm (26.67%)
- Gradient descent (19.17%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (27.50%)
- Artificial neural network (13.33%)
- Regret (8.33%)
In recent papers he was focusing on the following fields of study:
Quanquan Gu focuses on Artificial intelligence, Artificial neural network, Regret, Algorithm and Reinforcement learning.
In most of his Artificial intelligence studies, his work intersects topics such as Machine learning.
Many of his research projects under Artificial neural network are closely connected to Tangent and Quality with Tangent and Quality, tying the diverse disciplines of science together.
His work in the fields of Algorithm, such as Parameterized complexity, intersects with other areas such as Rate of convergence.
His studies examine the connections between Reinforcement learning and genetics, as well as such issues in Discrete mathematics, with regards to Stationary point and Constraint.
His Deep learning research includes elements of Normalization, Stochastic gradient descent, Regularization, Applied mathematics and Pattern recognition.
Between 2019 and 2021, his most popular works were:
- Improving Adversarial Robustness Requires Revisiting Misclassified Examples (76 citations)
- Gradient descent optimizes over-parameterized deep ReLU networks (50 citations)
- A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks (30 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Artificial intelligence
His primary areas of investigation include Artificial intelligence, Algorithm, Adversarial system, Artificial neural network and Reinforcement learning.
He has included themes like Optimization problem and Machine learning in his Artificial intelligence study.
His work deals with themes such as Gradient descent and Generalization, which intersect with Algorithm.
His study in Adversarial system is interdisciplinary in nature, drawing from both Maximization, Norm, Leverage and Minification.
His research investigates the link between Artificial neural network and topics such as Deep learning that cross with problems in Normalization, Pattern recognition and Kernel.
His Reinforcement learning research is multidisciplinary, incorporating elements of Discrete mathematics, Logarithm and Dimension.
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