What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Weinan Zhang mostly deals with Artificial intelligence, Machine learning, Discriminative model, Generative model and Reinforcement learning.
His research investigates the connection between Artificial intelligence and topics such as Pattern recognition that intersect with problems in Domain.
His study in Deep learning, Ranking, Recommender system and Leverage falls within the category of Machine learning.
His study looks at the relationship between Recommender system and fields such as Data mining, as well as how they intersect with chemical problems.
His Generative model study combines topics from a wide range of disciplines, such as Minimax and Natural language processing.
As a part of the same scientific family, Weinan Zhang mostly works in the field of Reinforcement learning, focusing on Closed captioning and, on occasion, World Wide Web.
His most cited work include:
- Seqgan: sequence generative adversarial nets with policy gradient (616 citations)
- SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient (609 citations)
- Efficient Architecture Search by Network Transformation. (296 citations)
What are the main themes of his work throughout his whole career to date?
Weinan Zhang mainly focuses on Artificial intelligence, Reinforcement learning, Machine learning, Recommender system and Information retrieval.
Many of his studies on Artificial intelligence apply to Data mining as well.
His Reinforcement learning study integrates concerns from other disciplines, such as Distributed computing, Advertising and Mathematical optimization.
His study on Ranking, Categorical variable and Ranking is often connected to Sequence as part of broader study in Machine learning.
The various areas that Weinan Zhang examines in his Recommender system study include Artificial neural network, Feature and Representation.
His study explores the link between Discriminative model and topics such as Generative model that cross with problems in Minimax.
He most often published in these fields:
- Artificial intelligence (40.82%)
- Reinforcement learning (26.59%)
- Machine learning (25.47%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (40.82%)
- Reinforcement learning (26.59%)
- Machine learning (25.47%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Reinforcement learning, Machine learning, Recommender system and Sample.
He interconnects Relation and Click-through rate in the investigation of issues within Artificial intelligence.
His Reinforcement learning research includes themes of Online advertising, Mathematical optimization, Leverage and Operations research.
His studies in Machine learning integrate themes in fields like Language model, Software deployment, Benchmark and Machine translation.
His research in Recommender system intersects with topics in Embedding, Feature, Theoretical computer science and Graph.
His study explores the link between Deep learning and topics such as Feature that cross with problems in Data mining.
Between 2019 and 2021, his most popular works were:
- Towards Making the Most of BERT in Neural Machine Translation (31 citations)
- GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation (22 citations)
- GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His primary scientific interests are in Artificial intelligence, Machine learning, Reinforcement learning, Recommender system and Mathematical optimization.
Artificial intelligence is closely attributed to Click-through rate in his research.
His work deals with themes such as Language model and Machine translation, which intersect with Machine learning.
His Reinforcement learning research integrates issues from Molecular graph, Information retrieval and Domain knowledge.
His research integrates issues of Embedding, Feature, Semantic similarity and Reachability in his study of Recommender system.
Weinan Zhang combines subjects such as Errors-in-variables models, Leverage and Dropout with his study of Mathematical optimization.
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