What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Yu Zhang focuses on Artificial intelligence, Machine learning, Multi-task learning, Unsupervised learning and Pattern recognition.
His work deals with themes such as Recommender system and Collaborative filtering, which intersect with Artificial intelligence.
His work on Interpretability and Rating matrix as part of general Machine learning study is frequently linked to Preference and Multi domain, therefore connecting diverse disciplines of science.
His study in Multi-task learning is interdisciplinary in nature, drawing from both Semi-supervised learning, Regularization, Outlier and Toy problem.
Supervised learning, Ubiquitous computing and Dimensionality reduction is closely connected to Reinforcement learning in his research, which is encompassed under the umbrella topic of Unsupervised learning.
His Pattern recognition research focuses on Face and how it connects with Range, Image and Projection.
His most cited work include:
- A Survey on Multi-Task Learning (383 citations)
- A convex formulation for learning task relationships in multi-task learning (298 citations)
- Learning from facial aging patterns for automatic age estimation (234 citations)
What are the main themes of his work throughout his whole career to date?
Yu Zhang spends much of his time researching Artificial intelligence, Machine learning, Multi-task learning, Pattern recognition and Transfer of learning.
His research brings together the fields of Natural language processing and Artificial intelligence.
His work investigates the relationship between Machine learning and topics such as Training set that intersect with problems in Representation.
Yu Zhang works mostly in the field of Multi-task learning, limiting it down to concerns involving Regularization and, occasionally, Mathematical optimization, Toy problem, Outlier, Relationship learning and Covariance matrix.
His Pattern recognition research incorporates themes from Subspace topology, Face, Kernel and Benchmark.
His research integrates issues of Recommender system, Collaborative filtering and Human–computer interaction in his study of Transfer of learning.
He most often published in these fields:
- Artificial intelligence (68.64%)
- Machine learning (38.98%)
- Multi-task learning (22.03%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (68.64%)
- Embedding (4.24%)
- Transfer of learning (15.25%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Embedding, Transfer of learning, Natural language processing and Benchmark.
His work carried out in the field of Artificial intelligence brings together such families of science as Multi-task learning and Pattern recognition.
He has included themes like Machine learning, Unsupervised learning, Cluster analysis and Convex combination in his Multi-task learning study.
Yu Zhang focuses mostly in the field of Transfer of learning, narrowing it down to matters related to Human–computer interaction and, in some cases, Recommender system.
His Natural language processing study combines topics from a wide range of disciplines, such as Probabilistic logic and Word embedding.
His Benchmark research is multidisciplinary, incorporating perspectives in Class, Artificial neural network and Feature vector.
Between 2019 and 2021, his most popular works were:
- Bi-Directional Recurrent Attentional Topic Model (3 citations)
- Fisher Deep Domain Adaptation (3 citations)
- A Survey on Multi-Task Learning (2 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Yu Zhang mostly deals with Artificial intelligence, Multi-task learning, Machine learning, Embedding and Natural language processing.
His research combines Pattern recognition and Artificial intelligence.
His research in the fields of Discriminative model and Class overlaps with other disciplines such as Separable space and Variance.
His Semantics research incorporates elements of Polysemy, Probabilistic logic and Word, Word embedding.
His Reinforcement learning research integrates issues from Unsupervised learning, Feature learning, Cluster analysis and Dimensionality reduction.
The Document modeling study combines topics in areas such as Sentence and Bayesian probability.
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