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, Machine learning, Deep learning, Convolutional neural network and Artificial neural network.
His research in the fields of Hebbian theory, Caffè and Variety overlaps with other disciplines such as Mobile device and Matching.
His study looks at the relationship between Hebbian theory and topics such as Object detection, which overlap with Feature extraction.
His Caffè research is multidisciplinary, incorporating perspectives in Embedding, Computer architecture and Theano.
His Variety study combines topics in areas such as Range, Visual recognition, Feature and Cognitive neuroscience of visual object recognition.
Yangqing Jia interconnects Distributed computing, Robotics and Inference in the investigation of issues within Artificial neural network.
His most cited work include:
- Going deeper with convolutions (19938 citations)
- Caffe: Convolutional Architecture for Fast Feature Embedding (8817 citations)
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (5091 citations)
What are the main themes of his work throughout his whole career to date?
Yangqing Jia mostly deals with Artificial intelligence, Machine learning, Pattern recognition, Deep learning and Artificial neural network.
His Artificial intelligence study frequently draws connections between related disciplines such as Computer vision.
His research integrates issues of Contextual image classification, Inference and Automatic image annotation in his study of Machine learning.
His studies examine the connections between Deep learning and genetics, as well as such issues in Computer architecture, with regards to Bottleneck.
His Artificial neural network research includes elements of Bayesian optimization, Computer engineering and Reinforcement learning.
His biological study deals with issues like Hebbian theory, which deal with fields such as Residual neural network.
He most often published in these fields:
- Artificial intelligence (82.26%)
- Machine learning (35.48%)
- Pattern recognition (27.42%)
What were the highlights of his more recent work (between 2016-2020)?
- Artificial intelligence (82.26%)
- Deep learning (20.97%)
- Computer engineering (6.45%)
In recent papers he was focusing on the following fields of study:
Yangqing Jia focuses on Artificial intelligence, Deep learning, Computer engineering, Speedup and Artificial neural network.
As part of his studies on Artificial intelligence, Yangqing Jia frequently links adjacent subjects like Machine learning.
In general Machine learning, his work in Support vector machine is often linked to Scheme, Matching and Generalization linking many areas of study.
His work carried out in the field of Deep learning brings together such families of science as Computer hardware and Computational science.
His research in Computer engineering intersects with topics in Bayesian optimization and Frame rate.
Artificial neural network and Reinforcement learning are commonly linked in his work.
Between 2016 and 2020, his most popular works were:
- Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour (1434 citations)
- FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search (390 citations)
- Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective (254 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Artificial intelligence, Machine learning, Inference, Deep learning and Artificial neural network are his primary areas of study.
His work on Enhanced Data Rates for GSM Evolution and Support vector machine as part of general Artificial intelligence research is frequently linked to FLOPS and Generalization, thereby connecting diverse disciplines of science.
His Support vector machine study frequently draws connections to adjacent fields such as Perspective.
His FLOPS research includes elements of Speedup, Mobile device, Frame rate and Computer engineering.
Among his research on Generalization, you can see a combination of other fields of science like Scheme and Matching.
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