What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Bo Chen spends much of his time researching Artificial intelligence, Pattern recognition, Deep learning, Artificial neural network and Algorithm.
His Artificial intelligence research is mostly focused on the topic Object detection.
The study incorporates disciplines such as Convolutional neural network and Pooling in addition to Object detection.
His work carried out in the field of Pattern recognition brings together such families of science as Ranking and Image.
His studies deal with areas such as Inference, Quantization and Dissipative system as well as Artificial neural network.
His study in Machine learning is interdisciplinary in nature, drawing from both Maximum likelihood, Separable space, Learning methods and Mobile vision.
His most cited work include:
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (6394 citations)
- MnasNet: Platform-Aware Neural Architecture Search for Mobile (873 citations)
- Learning Fine-Grained Image Similarity with Deep Ranking (664 citations)
What are the main themes of his work throughout his whole career to date?
Bo Chen mostly deals with Artificial intelligence, Pattern recognition, Algorithm, Metallurgy and Radar.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Computer vision.
His research in Pattern recognition is mostly focused on Convolutional neural network.
His study ties his expertise on Inference together with the subject of Algorithm.
His work on Metallurgy deals in particular with Austenitic stainless steel, Alloy, Microstructure and Grain boundary.
His Alloy research is within the category of Composite material.
He most often published in these fields:
- Artificial intelligence (30.34%)
- Pattern recognition (15.53%)
- Algorithm (14.08%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (30.34%)
- Alloy (8.74%)
- Composite material (9.71%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Alloy, Composite material, Pattern recognition and Topic model.
Artificial intelligence is closely attributed to Machine learning in his work.
The various areas that Bo Chen examines in his Alloy study include Titanium, Quenching, Oxide and Scanning electron microscope.
In the field of Composite material, his study on Ultimate tensile strength, Deformation, Superalloy and Microstructure overlaps with subjects such as Cathode ray.
His Pattern recognition research includes themes of Multispectral image and Cluster analysis.
His Convolutional neural network study combines topics in areas such as Range and Focus.
Between 2019 and 2021, his most popular works were:
- Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS (24 citations)
- Flexible Machine Learning-Based Cyberattack Detection Using Spatiotemporal Patterns for Distribution Systems (24 citations)
- MobileDets: Searching for Object Detection Architectures for Mobile Accelerators (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Artificial intelligence, Machine learning, Deep learning, Microgrid and Recurrent neural network.
His work in the fields of Artificial intelligence, such as Convolutional neural network and Contextual image classification, intersects with other areas such as Scalability.
Convolutional neural network is a primary field of his research addressed under Pattern recognition.
His Machine learning research includes elements of Search engine, Laplacian matrix, Bayes classifier, Thesaurus and Machine translation.
His work in Deep learning tackles topics such as Detector which are related to areas like Algorithm, Modulation and Scale.
His biological study spans a wide range of topics, including Adversarial system, Text corpus, Natural language processing and Compressive imaging.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
