What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
His primary areas of investigation include Artificial intelligence, Pattern recognition, Contextual image classification, Artificial neural network and Convolutional neural network.
His study of Object detection is a part of Artificial intelligence.
In his work, Kernel is strongly intertwined with Machine learning, which is a subfield of Pattern recognition.
Xiangyu Zhang usually deals with Artificial neural network and limits it to topics linked to Test set and Task, Feature learning, MNIST database and Softmax function.
His Convolutional neural network research is multidisciplinary, relying on both Image resolution, Computer vision, Stochastic gradient descent and Speedup.
His study in Computer vision is interdisciplinary in nature, drawing from both Transfer of learning and Deep learning, Transformer.
His most cited work include:
- Deep Residual Learning for Image Recognition (61800 citations)
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification (7908 citations)
- Identity Mappings in Deep Residual Networks (4287 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Object detection, Pattern recognition, Segmentation and Computer vision are his primary areas of study.
His Artificial intelligence study combines topics in areas such as Machine learning and Code.
His Object detection research is multidisciplinary, incorporating elements of Algorithm, Feature and Task.
His Pattern recognition study integrates concerns from other disciplines, such as Image resolution, Visual recognition, Spatial analysis and Residual.
Within one scientific family, Xiangyu Zhang focuses on topics pertaining to Speedup under Convolutional neural network, and may sometimes address concerns connected to Computation.
Xiangyu Zhang has included themes like Normalization, Deep learning and Pruning in his Artificial neural network study.
He most often published in these fields:
- Artificial intelligence (69.15%)
- Object detection (42.55%)
- Pattern recognition (36.17%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (69.15%)
- Pattern recognition (36.17%)
- Code (19.15%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Code, Object detection and Segmentation.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Margin, Machine learning and Markov chain.
In his study, Identification is strongly linked to Spatial analysis, which falls under the umbrella field of Pattern recognition.
His Code research integrates issues from Relation, Convolutional neural network and Feature vector.
The various areas that Xiangyu Zhang examines in his Convolutional neural network study include Contextual image classification and Parallel computing.
His Object detection research includes themes of Algorithm, Encoder and Feature.
Between 2019 and 2021, his most popular works were:
- Learning Human-Object Interaction Detection Using Interaction Points (31 citations)
- Learning Dynamic Routing for Semantic Segmentation (24 citations)
- TOWARDS STABILIZING BATCH STATISTICS IN BACKWARD PROPAGATION OF BATCH NORMALIZATION (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Xiangyu Zhang focuses on Artificial intelligence, Pattern recognition, Code, Segmentation and Object.
His multidisciplinary approach integrates Artificial intelligence and Source code in his work.
His work deals with themes such as Representation and Spatial analysis, which intersect with Pattern recognition.
The concepts of his Code study are interwoven with issues in Convolutional neural network and Parallel computing.
Xiangyu Zhang does research in Object, focusing on Object detection specifically.
Object detection is the subject of his research, which falls under Computer vision.
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