What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Peilin Zhao mainly focuses on Artificial intelligence, Machine learning, Online machine learning, Data mining and Algorithm.
His Artificial intelligence study frequently links to related topics such as Pattern recognition.
Transfer of learning, Training set and Feature is closely connected to Key in his research, which is encompassed under the umbrella topic of Machine learning.
In Data mining, Peilin Zhao works on issues like Statistical classification, which are connected to Binary classification.
His research investigates the connection between Feature learning and topics such as Representation that intersect with issues in Deep learning.
His work focuses on many connections between Feature extraction and other disciplines, such as Codebook, that overlap with his field of interest in Contextual image classification.
His most cited work include:
- Local features are not lonely – Laplacian sparse coding for image classification (413 citations)
- Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life (255 citations)
- Stochastic Optimization with Importance Sampling for Regularized Loss Minimization (181 citations)
What are the main themes of his work throughout his whole career to date?
Peilin Zhao mostly deals with Artificial intelligence, Machine learning, Online machine learning, Data mining and Mathematical optimization.
His Artificial intelligence study incorporates themes from Algorithm design and Pattern recognition.
His work on Active learning, Semi-supervised learning and Regret is typically connected to Multi-task learning as part of general Machine learning study, connecting several disciplines of science.
His Data mining research is multidisciplinary, incorporating elements of Margin, Statistical classification and Maximization.
His Mathematical optimization study integrates concerns from other disciplines, such as Computational complexity theory, Sampling and Estimator.
His work deals with themes such as Continuous-time stochastic process and Stochastic optimization, which intersect with Sampling.
He most often published in these fields:
- Artificial intelligence (65.00%)
- Machine learning (55.00%)
- Online machine learning (13.89%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (65.00%)
- Machine learning (55.00%)
- Deep learning (6.11%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Machine learning, Deep learning, Architecture and Theoretical computer science are his primary areas of study.
While working in this field, Peilin Zhao studies both Artificial intelligence and Field.
His study connects Cover and Machine learning.
The concepts of his Theoretical computer science study are interwoven with issues in Social media, Recurrent neural network and Directed graph.
In his study, Mathematical optimization is strongly linked to Range, which falls under the umbrella field of Pareto principle.
His Supervised learning research includes themes of Unsupervised learning and Categorization.
Between 2019 and 2021, his most popular works were:
- FedML: A Research Library and Benchmark for Federated Machine Learning. (31 citations)
- Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis (24 citations)
- Towards Fast Adaptation of Neural Architectures with Meta Learning (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Peilin Zhao focuses on Artificial intelligence, Machine learning, Deep learning, Recurrent neural network and Theoretical computer science.
His work on Reinforcement learning and Control as part of general Artificial intelligence study is frequently linked to Source code, Edge device and Baseline, bridging the gap between disciplines.
His Control research integrates issues from Asset, Selection and Portfolio.
His research on Machine learning frequently links to adjacent areas such as Benchmark.
His Classifier research extends to Deep learning, which is thematically connected.
His Recurrent neural network study combines topics in areas such as Social media and Directed graph.
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