What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Metallurgy
Artificial intelligence, Machine learning, Artificial neural network, Metallurgy and Deep learning are his primary areas of study.
His research integrates issues of Scalability and Search algorithm in his study of Artificial intelligence.
Vijay K. Vasudevan combines subjects such as Inference, CUDA, Dataflow, Computation and Multi-core processor with his study of Machine learning.
As part of one scientific family, he deals mainly with the area of Artificial neural network, narrowing it down to issues related to the Word error rate, and often Rotation, Image processing, Image, Computer vision and Regularization.
His Metallurgy research is multidisciplinary, incorporating elements of Volume fraction and Crystal structure.
The study incorporates disciplines such as Recurrent neural network, String, Distributed computing and Reinforcement learning in addition to Deep learning.
His most cited work include:
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (5091 citations)
- TensorFlow: a system for large-scale machine learning (4961 citations)
- Learning Transferable Architectures for Scalable Image Recognition (2224 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Metallurgy, Microstructure, Alloy, Composite material and Crystallography.
His Metallurgy study integrates concerns from other disciplines, such as Volume fraction and Stress.
His Microstructure study incorporates themes from Hardening, Nucleation, Surface modification and Dislocation.
The Alloy study combines topics in areas such as Desorption and Lamellar structure.
His Crystallography study combines topics from a wide range of disciplines, such as Electron diffraction, Transmission electron microscopy and Intermetallic.
Vijay K. Vasudevan has researched Intermetallic in several fields, including Deformation mechanism and Titanium alloy.
He most often published in these fields:
- Metallurgy (38.43%)
- Microstructure (31.94%)
- Alloy (22.22%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (12.04%)
- Composite material (19.91%)
- Microstructure (31.94%)
In recent papers he was focusing on the following fields of study:
Vijay K. Vasudevan mostly deals with Artificial intelligence, Composite material, Microstructure, Object detection and Residual stress.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Search algorithm.
His study in Microstructure is interdisciplinary in nature, drawing from both Copper, Laser and Dislocation.
Vijay K. Vasudevan interconnects Hardening, Nanocrystal, Work hardening and Surface modification in the investigation of issues within Residual stress.
His studies examine the connections between Artificial neural network and genetics, as well as such issues in Word error rate, with regards to Rotation, Image processing, Image, Contextual image classification and Regularization.
His Latency research also works with subjects such as
- Mobile device, which have a strong connection to Deep learning,
- Task which is related to area like Computer data storage.
Between 2017 and 2021, his most popular works were:
- Learning Transferable Architectures for Scalable Image Recognition (2224 citations)
- MnasNet: Platform-Aware Neural Architecture Search for Mobile (873 citations)
- AutoAugment: Learning Augmentation Policies from Data (568 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Composite material
Vijay K. Vasudevan spends much of his time researching Artificial intelligence, Object detection, Lidar, Search algorithm and Computer engineering.
Much of his study explores Artificial intelligence relationship to Machine learning.
His Machine learning research integrates issues from Image processing, Image and Rotation.
His Lidar study integrates concerns from other disciplines, such as Generalization, Scalability and Data mining.
His Search algorithm research focuses on Pattern recognition and how it relates to Next-generation network.
His Computer engineering research is multidisciplinary, relying on both Latency, Convolutional neural network, Mobile device and Task.
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