His primary scientific interests are in Artificial intelligence, Machine learning, Data mining, Hidden Markov model and Algorithm. His work on Deep learning as part of general Artificial intelligence research is frequently linked to Predictive medicine, bridging the gap between disciplines. His biological study spans a wide range of topics, including Subspace topology and Inference.
His work carried out in the field of Data mining brings together such families of science as Pragmatics and Naive Bayes classifier. His Hidden Markov model study integrates concerns from other disciplines, such as Hidden semi-Markov model and Activity recognition. His Algorithm research includes elements of Relation and Personalization.
His primary areas of study are Artificial intelligence, Machine learning, Data mining, Cluster analysis and Pattern recognition. His work on Artificial intelligence deals in particular with Inference, Deep learning, Probabilistic logic, Hidden Markov model and Boltzmann machine. His Collaborative filtering, Activity recognition and Artificial neural network study in the realm of Machine learning interacts with subjects such as Structure and Data modeling.
His Data mining study incorporates themes from Context, Subspace topology, Hierarchical Dirichlet process, Mixture model and Feature selection. His work on Correlation clustering as part of general Cluster analysis research is often related to Scalability, thus linking different fields of science. His Pattern recognition research incorporates elements of Clustering high-dimensional data, Representation and Feature.
His primary areas of investigation include Artificial intelligence, Machine learning, Embedding, Theoretical computer science and Deep learning. Dinh Phung has researched Artificial intelligence in several fields, including Data mining and Pattern recognition. His study in the fields of Reinforcement learning under the domain of Machine learning overlaps with other disciplines such as Named-entity recognition.
His research on Embedding also deals with topics like
Dinh Phung mainly investigates Artificial intelligence, Machine learning, Theoretical computer science, Adversarial system and Transformer. His Artificial intelligence research integrates issues from Divergence and Pattern recognition. His research in Machine learning intersects with topics in Heuristics and Heuristic.
His studies in Theoretical computer science integrate themes in fields like Graph neural networks, Graph and Convolutional neural network. His Adversarial system research is multidisciplinary, incorporating elements of Classifier and Generative grammar. His Transformer study also includes
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Activity recognition and abnormality detection with the switching hidden semi-Markov model
T.V. Duong;H.H. Bui;D.Q. Phung;S. Venkatesh.
computer vision and pattern recognition (2005)
Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter
Ba-Ngu Vo;Ba-Tuong Vo;Dinh Phung.
IEEE Transactions on Signal Processing (2014)
Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model
N.T. Nguyen;D.Q. Phung;S. Venkatesh;H. Bui.
computer vision and pattern recognition (2005)
Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.
Wei Luo;Dinh Phung;Truyen Tran;Sunil Gupta.
Journal of Medical Internet Research (2016)
Predicting healthcare trajectories from medical records: A deep learning approach.
Trang Pham;Truyen Tran;Dinh Q. Phung;Svetha Venkatesh.
Journal of Biomedical Informatics (2017)
DeepCare: A Deep Dynamic Memory Model forźPredictive Medicine
Trang Pham;Truyen Tran;Dinh Phung;Svetha Venkatesh.
knowledge discovery and data mining (2016)
A novel embedding model for knowledge base completion based on convolutional neural network
Dai Quoc Nguyen;Tu Dinh Nguyen;Dat Quoc Nguyen;Dinh Q. Phung.
north american chapter of the association for computational linguistics (2018)
MGAN: Training Generative Adversarial Nets with Multiple Generators
Quan Hoang;Tu Dinh Nguyen;Trung Le;Dinh Phung.
international conference on learning representations (2018)
Affective and Content Analysis of Online Depression Communities
Thin Nguyen;Dinh Phung;Bo Dao;Svetha Venkatesh.
IEEE Transactions on Affective Computing (2014)
Efficient duration and hierarchical modeling for human activity recognition
Thi Duong;Dinh Phung;Hung Bui;Svetha Venkatesh.
Artificial Intelligence (2009)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: