What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Rong Jin mainly focuses on Artificial intelligence, Machine learning, Data mining, Information retrieval and Pattern recognition.
Artificial intelligence and Optimization problem are commonly linked in his work.
His study in the fields of Support vector machine under the domain of Machine learning overlaps with other disciplines such as Set.
Rong Jin interconnects Contextual image classification, Learning to rank and Collaborative filtering in the investigation of issues within Data mining.
His Information retrieval research includes themes of Language model, Visual Word and Text mining.
His Pattern recognition research is multidisciplinary, incorporating elements of Histogram, Cognitive neuroscience of visual object recognition and Categorization.
His most cited work include:
- Understanding bag-of-words model: A statistical framework (394 citations)
- Batch mode active learning and its application to medical image classification (319 citations)
- Combining link and content for community detection: a discriminative approach (305 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Machine learning, Mathematical optimization, Pattern recognition and Information retrieval.
His research in Artificial intelligence tackles topics such as Data mining which are related to areas like Collaborative filtering.
As part of his studies on Machine learning, Rong Jin often connects relevant subjects like Empirical research.
As a member of one scientific family, Rong Jin mostly works in the field of Mathematical optimization, focusing on Rate of convergence and, on occasion, Stochastic optimization.
His study in Kernel method, Feature extraction and Kernel are all subfields of Pattern recognition.
His study in the field of Human–computer information retrieval also crosses realms of Set.
He most often published in these fields:
- Artificial intelligence (51.65%)
- Machine learning (29.23%)
- Mathematical optimization (18.46%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (51.65%)
- Mathematical optimization (18.46%)
- Deep learning (3.96%)
In recent papers he was focusing on the following fields of study:
Rong Jin mainly investigates Artificial intelligence, Mathematical optimization, Deep learning, Machine learning and Computer vision.
His study in Artificial intelligence concentrates on Softmax function, Image, Feature learning, Artificial neural network and Contextual image classification.
The study incorporates disciplines such as E-commerce and Ranking, Information retrieval, Relevance in addition to Feature learning.
His study looks at the intersection of Mathematical optimization and topics like Rate of convergence with Non convex optimization.
His Deep learning research is multidisciplinary, relying on both Algorithm and Benchmark.
With his scientific publications, his incorporates both Machine learning and Scheme.
Between 2018 and 2021, his most popular works were:
- On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization. (89 citations)
- SoftTriple Loss: Deep Metric Learning Without Triplet Sampling (68 citations)
- XNAS: Neural Architecture Search with Expert Advice (52 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Artificial intelligence, Mathematical optimization, Artificial neural network, Deep learning and Speedup.
His study looks at the relationship between Artificial intelligence and fields such as Machine learning, as well as how they intersect with chemical problems.
His Mathematical optimization research includes elements of Rate of convergence, Convergence, Convex function and Robustness.
His biological study spans a wide range of topics, including Entropy estimation, Prior probability, Leverage and Image compression, Data compression ratio.
In his research on the topic of Deep learning, Feature, Distributed computing and Sampling is strongly related with Benchmark.
His research on Speedup also deals with topics like
- Stochastic gradient descent, which have a strong connection to Big data and Parallel computing,
- Communication complexity that intertwine with fields like Computation and Stochastic optimization.
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