Lecturer

Department of Information Systems (IS)
School of Information and Communication Technology (SOICT)
Hanoi University of Science and Technology (HUST)

Research

Interests

Publications

Journal

  1. Ha Nguyen, Hoang Pham, Son Nguyen, Linh Ngo Van, Khoat Than. “Adaptive Infinite Dropout for Noisy and Sparse Data Streams,” Machine Learning journal, 2022.
  2. Quyen Tran, Lam Tran, Linh Chu Hai, Linh Ngo, Khoat Than. “From Implicit to Explicit feedback: A deep neural network for modeling sequential behaviors and long-short term preferences of online users”. Neurocomputing, 2022.
  3. Bach, Tran Xuan, Nguyen Duc Anh, Linh Ngo Van, and Khoat Than. “Dynamic transformation of prior knowledge into Bayesian models for data streams.” IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022.
  4. Linh Ngo Van, Bach Tran, and Khoat Than. “A graph convolutional topic model for short and noisy text streams” Neurocomputing, Volume 468, 11 January 2022, Pages 345-359.
  5. Dieu Vu, Khang Truong, Khanh Nguyen, Linh Ngo Van, Khoat Than, “Revisiting Supervised Word Embeddings”, Journal of Information Science and Engineering, 2022.
  6. Tien-Cuong Nguyen, Van-Quyen Nguyen, Van-Linh Ngo, Khoat Than, Tien-Lam Pham. “Learning Hidden Chemistry with Deep Neural Networks”. Computational Materials Science journal, Volume 200, December 2021, 110784. Elsevier.
  7. Duc-Anh Nguyen, Ngo Van Linh, Nguyen Kim Anh, Canh Hao Nguyen, Khoat Than, “Boosting prior knowledge in streaming variational Bayes,” Neurocomputing, Volume 424, 1 February 2021, Pages 143-159.
  8. Anh Tuan Phan, Bach Tran, Thien Huu Nguyen, Linh Van Ngo, Khoat Than, “Bag of biterms modeling for short texts”, Knowledge and Information Systems (KAIS),62, pages 4055–4090 (2020).
  9. N. Van Linh, D. A. Nguyen, T. B. Nguyen and K. Than, “Neural Poisson Factorization,” IEEE Access, 2020.
  10. Cuong Ha-Nhat, Dang Tran, Linh Ngo Van, Khoat Than, “Eliminating overfitting of probabilistic topic models on short and noisy text: The role of Dropout”, International Journal of Approximate Reasoning, Springer, 2019.
  11. Hoa Le Minh, Son Ta Cong, Quyen Pham The, Ngo Van Linh, Khoat Than, “Collaborative Topic Model for Poisson distributed ratings”, International Journal of Approximate Reasoning (IJA), 2018.
  12. Ngo Van Linh, Nguyen Kim Anh, Khoat Than, Chien Nguyen Dang, “An Effective and Interpretable Method for Document Classification”, Knowledge and Information Systems journal (KAIS), 50(3), 763-793, 2017.

Conference

  1. Linh Ngo Van, Nam Le Hai, Hoang Pham, Khoat Than. “Auxiliary Local Variables for Improving Regularization/prior Approach in Continual Learning”. Advances in Knowledge Discovery and Data Mining. PAKDD 2022.
  2. Hoang Phan Viet, Anh Phan Tuan, Son Nguyen, Linh Ngo Van, Khoat Than. “Reducing catastrophic forgetting in neural networks via Gaussian mixture approximation”. Advances in Knowledge Discovery and Data Mining. PAKDD 2022.
  3. Hieu Man Duc Trong, Nghia Ngo Trung, Linh Ngo Van and Thien Huu Nguyen "Selecting Optimal Context Sentences for Event-Event Relation Extraction" Proceedings of AAAI 2022, Vancouver, Canada, February 2022
  4. Tuc Nguyen, Linh Ngo Van, Khoat Than, “Modeling the sequential behaviors of online users in recommender systems”, In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications. 2020.
  5. Van-Son Nguyen, Duc-Tung Nguyen, Linh Ngo Van, Khoat Than, “Infinite Dropout for training Bayesian models from data streams”, In Proceedings of IEEE International Conference on Big Data (BigData 2019), Los Angeles, CA, USA, 2019.
  6. Tuan Anh Phan, Nhat Nguyen Trong, Duong Bui, Linh Van Ngo, and Khoat Than, “From Implicit to Explicit Feedback: A deep neural network for modeling the sequential behaviors of online users”, In Proceeding of the Asian Conference on Machine Learning (ACML), 2019.
  7. Linh The Nguyen, Linh Van Ngo, Khoat Than and Thien Huu Nguyen, “Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings”, In Proceeding of the Association for Computational Linguistics (ACL), 2019.
  8. Thanh Hai Hoang, Anh Phan Tuan, Linh Ngo Van, Khoat Than, “Enriching user representation in Neural Matrix Factorization”, In Proceedings of RIVF. IEEE, 2019.
  9. Khoat Than, Xuan Bui, Tung Nguyen-Trong, Khang Truong, Son Nguyen, Bach Tran, Linh Ngo Van, and Anh Nguyen-Duc. “How to make a machine learn continuously: a tutorial of the Bayesian approach.” In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, vol. 11006, p. 110060I. 2019.
  10. Duc-Anh Nguyen, Kim Anh Nguyen, Ngo Van Linh, Khoat Than, “Keeping priors in streaming Bayesian learning”, In Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 247-258, 2017.
  11. Vu Le, Chien Phung, Cuong Vu, Ngo Van Linh, Khoat Than, “Streaming Aspect-Sentiment Analysis”, in Proceeding of the International Conference on Computing and Communication Technologies (RIVF), pp 181-186, 2016
  12. Mai Tien Khai, Mai Anh Sang, Nguyen Kim Anh, Ngo Van Linh, Khoat Than, “Enabling Hierarchical Dirichlet Processes to work better for short texts at large scale” in Proceeding of the 20th Pacific-Asia Conference (PAKDD), pp. 431-442, 2016.
  13. Ngo Van Linh, Nguyen Kim Anh, Khoat Than, and Nguyen Nguyen Tat. ”Effective and Interpretable Document Classification Using Distinctly Labeled Dirichlet Process Mixture Models of von Mises-Fisher Distributions” In proceeding of 20th Database Systems for Advanced Applications Conference (DASFAA), pp. 139-153, 2015.
  14. Ngo Van Linh, Nguyen Kim Anh, and Khoat Than. "An Effective NMF-Based Method for Supervised Dimension Reduction" Knowledge and Systems Engineering (KSE), pp.93-104, 2015.
  15. Anh Nguyen Kim, Nguyen Khac Toi, and Ngo Van Linh. "An interpretable method for text summarization based on simplicial non-negative matrix factorization" in Proceeding of the Symposium on Information and Communication Technology (SoICT), pp.57-64, 2014.
  16. Nguyen Thi Kim Anh, Ngo Van Linh, Nguyen Khac Toi, Nguyen The Tam, "Multi-labeled Document Classiffication using Semi-supervised Mixture Model of Watson distributions on Document Manifold" In proceeding of the International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp.129-134, 2013.
  17. Nguyen Thi Kim Anh, Nguyen The Tam, Ngo Van Linh, "Document Clustering using Mixture Model of von Mises-Fisher Distributions on Document Manifold" In proceeding of the International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 146-151, 2013.
  18. Nguyen Kim Anh, Ngo Van Linh, Le Hong Ky, Nguyen The Tam, "Document classiffication using semi-supervised mixture model of von Mises-Fisher distributions on document manifold" In proceeding of the Symposium on Information and Communication Technology (SoICT), pp. 94-100, 2013.
  19. Nguyen Kim Anh, Nguyen The Tam, Ngo Van Linh, "Document clustering using dirichlet process mixture model of von Mises-Fisher distributions" In proceeding of the Symposium on Information and Communication Technology (SoICT), pp. 131-138, 2013.
  20. Ngo Van Linh, Nguyen Kim Anh, Cao Manh Dat, "Improving Vietnamese Web Page Classiffication by Combining Hybrid Feature Selection and Label Propagation with Link Information" In Proceeding of the International Conference on Context-Aware Systems and Applications (ICCASA), pp.324-334, 2012.
  21. Nguyen Kim Anh, Vu Minh Thanh, Ngo Van Linh, "Efficient label propagation for classiffication on information networks" In proceeding of the Symposium on Information and Communication Technology (SoICT), pp. 41-46, 2012.

Teaching