What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Pattern recognition, Mathematical optimization, Machine learning and Dimensionality reduction.
His research brings together the fields of Multi-task learning and Artificial intelligence.
His research in Pattern recognition intersects with topics in Conditional probability, Conditional probability distribution and Marginal distribution.
Jieping Ye combines subjects such as Regularization, Matrix completion and Convex optimization with his study of Mathematical optimization.
His research integrates issues of Dementia and Cognition in his study of Machine learning.
Jieping Ye has included themes like Algorithm, Scatter matrix and Computer vision in his Dimensionality reduction study.
His most cited work include:
- Tensor completion for estimating missing values in visual data (1038 citations)
- Two-Dimensional Linear Discriminant Analysis (565 citations)
- An accelerated gradient method for trace norm minimization (472 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Machine learning, Pattern recognition, Mathematical optimization and Data mining are his primary areas of study.
His study in Multi-task learning extends to Artificial intelligence with its themes.
His work focuses on many connections between Machine learning and other disciplines, such as Neuroimaging, that overlap with his field of interest in Disease.
Feature extraction and Principal component analysis are among the areas of Pattern recognition where Jieping Ye concentrates his study.
His Mathematical optimization study combines topics in areas such as Convex optimization and Algorithm, Regularization.
His studies deal with areas such as Singular value decomposition and QR decomposition as well as Linear discriminant analysis.
He most often published in these fields:
- Artificial intelligence (47.28%)
- Machine learning (23.97%)
- Pattern recognition (16.99%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (47.28%)
- Machine learning (23.97%)
- Deep learning (5.88%)
In recent papers he was focusing on the following fields of study:
Jieping Ye mostly deals with Artificial intelligence, Machine learning, Deep learning, Reinforcement learning and Key.
His Artificial intelligence study integrates concerns from other disciplines, such as Margin and Pattern recognition.
His Pattern recognition research incorporates themes from Frame and Background noise.
His studies in Machine learning integrate themes in fields like Neuroimaging, Coding, Inference and Cognitive decline.
The Deep learning study combines topics in areas such as Intelligent transportation system, Data mining, Artificial neural network, Scale and Estimated time of arrival.
The concepts of his Key study are interwoven with issues in Matching, Price elasticity of demand, Supply and demand and Social optimum.
Between 2019 and 2021, his most popular works were:
- A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications (77 citations)
- AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates. (37 citations)
- Optimizing matching time interval and matching radius in on-demand ride-sourcing markets (15 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, Reinforcement learning, Key, Graph and Machine learning.
His Artificial intelligence research is multidisciplinary, incorporating elements of Margin and Pattern recognition.
His biological study spans a wide range of topics, including Order, Combinatorial optimization and Bipartite graph.
Jieping Ye focuses mostly in the field of Key, narrowing it down to topics relating to Matching and, in certain cases, Mathematical optimization, Interval and Control variable.
The study incorporates disciplines such as Theoretical computer science, Data mining and Graph in addition to Graph.
His Machine learning research integrates issues from Neuroimaging, Speedup and Heuristic.
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