What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His primary areas of investigation include Artificial intelligence, Machine learning, Information retrieval, Scheme and Question answering.
His Artificial intelligence study frequently draws connections to adjacent fields such as Pattern recognition.
His work in the fields of Machine learning, such as Collaborative filtering, Activity recognition and Transduction, intersects with other areas such as Matrix decomposition.
His Information retrieval study incorporates themes from Ranking, Multimedia and World Wide Web.
His biological study spans a wide range of topics, including Visualization and Knowledge organization.
Liqiang Nie works mostly in the field of Deep learning, limiting it down to topics relating to Recommender system and, in certain cases, Artificial neural network, Key, Information overload and Hybrid system, as a part of the same area of interest.
His most cited work include:
- Neural Collaborative Filtering (1578 citations)
- SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning (731 citations)
- Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention (425 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Machine learning, Information retrieval, Pattern recognition and Theoretical computer science.
His Artificial intelligence study integrates concerns from other disciplines, such as Computer vision and Natural language processing.
His Artificial neural network and Discriminative model study, which is part of a larger body of work in Machine learning, is frequently linked to Consistency and Matrix decomposition, bridging the gap between disciplines.
His Artificial neural network research is multidisciplinary, incorporating perspectives in Metric and Feature vector.
The Question answering and Recommender system research Liqiang Nie does as part of his general Information retrieval study is frequently linked to other disciplines of science, such as Preference, therefore creating a link between diverse domains of science.
His research investigates the link between Theoretical computer science and topics such as Hash function that cross with problems in Algorithm.
He most often published in these fields:
- Artificial intelligence (43.46%)
- Machine learning (23.63%)
- Information retrieval (19.41%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (43.46%)
- Machine learning (23.63%)
- Theoretical computer science (10.13%)
In recent papers he was focusing on the following fields of study:
Liqiang Nie mostly deals with Artificial intelligence, Machine learning, Theoretical computer science, Recommender system and Information retrieval.
His Pattern recognition research extends to the thematically linked field of Artificial intelligence.
His study looks at the relationship between Machine learning and topics such as Feature extraction, which overlap with Visualization.
His Theoretical computer science study combines topics in areas such as Embedding, Hash function, Laplacian matrix and Cluster analysis.
His Recommender system research incorporates elements of Data science, Aggregate, Leverage and Similarity.
Liqiang Nie has included themes like Key and Semantic gap in his Information retrieval study.
Between 2019 and 2021, his most popular works were:
- Graph Convolutional Network Hashing (39 citations)
- Neural Multimodal Cooperative Learning Toward Micro-Video Understanding (31 citations)
- Scalable Deep Hashing for Large-Scale Social Image Retrieval (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Hash function, Embedding and Theoretical computer science.
His work on Deep learning and Modality as part of general Artificial intelligence research is frequently linked to Rank, thereby connecting diverse disciplines of science.
His biological study deals with issues like Manifold regularization, which deal with fields such as Convolutional neural network.
The study incorporates disciplines such as Feature extraction and Noise reduction in addition to Machine learning.
His research integrates issues of Algorithm and Image retrieval in his study of Hash function.
The concepts of his Embedding study are interwoven with issues in Question answering, Similarity measure and Directed acyclic graph.
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