What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Artificial intelligence, Mathematical optimization, Artificial neural network, Control theory and Pattern recognition.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Computer vision.
His research in Mathematical optimization intersects with topics in Matching, Graduated optimization and Partial permutation.
Hong Qiao works mostly in the field of Artificial neural network, limiting it down to concerns involving Exponential stability and, occasionally, Recurrent neural network, Applied mathematics and Lipschitz continuity.
His biological study spans a wide range of topics, including Stochastic process, Bilinear interpolation and Matrix.
His Discriminative model study in the realm of Pattern recognition interacts with subjects such as Competitive algorithm.
His most cited work include:
- On stabilization of bilinear uncertain time-delay stochastic systems with Markovian jumping parameters (236 citations)
- Event-Triggered State Estimation for Discrete-Time Multidelayed Neural Networks With Stochastic Parameters and Incomplete Measurements (127 citations)
- A reference model approach to stability analysis of neural networks (123 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Machine learning are his primary areas of study.
Feature extraction, Feature, Support vector machine, Object detection and Image are the core of his Artificial intelligence study.
His Computer vision research incorporates elements of Robustness and GRASP.
He combines subjects such as Contextual image classification and Cluster analysis with his study of Pattern recognition.
His research in Machine learning is mostly concerned with Artificial neural network.
His Matching research is multidisciplinary, incorporating elements of Combinatorics and Relaxation.
He most often published in these fields:
- Artificial intelligence (63.43%)
- Computer vision (32.00%)
- Pattern recognition (24.86%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (63.43%)
- Robot (10.00%)
- Computer vision (32.00%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Robot, Computer vision, Pattern recognition and Artificial neural network.
Many of his studies on Artificial intelligence apply to Machine learning as well.
Hong Qiao has included themes like Robotic arm and Robustness in his Robot study.
His Computer vision study integrates concerns from other disciplines, such as Selection method, Moment and Benchmark.
The concepts of his Pattern recognition study are interwoven with issues in Subspace topology and Data set.
His Artificial neural network study combines topics from a wide range of disciplines, such as Encoding, State space, Forgetting, Image and Salient object detection.
Between 2018 and 2021, his most popular works were:
- Cross-modality interactive attention network for multispectral pedestrian detection (24 citations)
- Guided Policy Search for Sequential Multitask Learning (19 citations)
- A Survey of Methods and Strategies for High-Precision Robotic Grasping and Assembly Tasks—Some New Trends (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Artificial intelligence, Pattern recognition, Reinforcement learning, Algorithm and Artificial neural network.
His study ties his expertise on Computer vision together with the subject of Artificial intelligence.
His Pattern recognition research is multidisciplinary, relying on both Focus, Metric and Laplace operator.
Hong Qiao has researched Reinforcement learning in several fields, including Control and Human–computer interaction.
His Algorithm research incorporates themes from Rough set, Task and Outlier.
His studies deal with areas such as Robot and Robustness as well as Artificial neural network.
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