Publications

2024

  1. Viet Nguyen, Giang Vu, Tung Nguyen Thanh, Toan Tran, Khoat Than, “On inference stability for diffusion models”. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024.
  2. Thanh-Thien Le, Manh Nguyen, Tung Thanh Nguyen, Linh Van Ngo, Thien Huu Nguyen. “Continual Relation Extraction via Sequential Multi-task Learning”. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024.
  3. Tinh Son Luong, Thanh-Thien Le, Linh Ngo Van, Thien Huu Nguyen, “Realistic Evaluation of Toxicity in Large Language Models”, Findings of the Association for Computational Linguistics, 2024.
  4. Nam Le, Trang Thien, Linh Ngo Van, Thien Nguyen, Khoat Than. “Continual Variational Dropout: A View of Auxiliary Local Variables in Continual Learning.” Machine Learning journal, Volume 113, pages 281-323, 2024.
  5. Tung Doan, Tuan Phan, Phu Nguyen, Khoat Than, Muriel Visani, Atsuhiro Takasu, “Partial Ordered Wasserstein Distance for Sequential Data”, Neurocomputing, 2024.
  6. Thanh-Thien Le, Viet Dao, Linh Van Nguyen, Thi-Nhung Nguyen, Linh Van Ngo, and Thien Huu Nguyen, “SharpSeq: Empowering Continual Event Detection through Sharpness-Aware Sequential-task Learning”, in Proceedings of NAACL-HLT, 2024
  7. Viet Dao, Van-Cuong Pham, Quyen Tran, Thanh-Thien Le, Linh Ngo Van, Thien Huu Nguyen, “Lifelong Event Detection via Optimal Transport”, In Proceeding of the Conference on Empirical Methods in Natural Language Processing, 2024
  8. Quyen Tran, Nguyen Xuan Thanh, Nguyen Hoang Anh, Nam Le Hai, Trung Le, Linh Van Ngo, Thien Huu Nguyen, “Preserving Generalization of Language models in Few-shot Continual Relation Extraction”, In Proceeding of the Conference on Empirical Methods in Natural Language Processing, 2024
  9. Duy-Tung Pham, Thien Trang Nguyen Vu, Tung Nguyen, Linh Van Ngo, Duc Anh Nguyen, Thien Huu Nguyen, “NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization”, In Findings of the Conference on Empirical Methods in Natural Language Processing, 2024

 

2023

  1. Nam Le, Trang Thien, Linh Ngo Van, Thien Nguyen, Khoat Than. “Continual Variational Dropout: A View of Auxiliary Local Variables in Continual Learning.” Machine Learning journal, 2023.
  2. Tung Nguyen, Tung Pham, Linh Ngo Van, Ha-Bang Ban, and Khoat Than. “Out-of-Vocabulary Handling and Topic Quality Control Strategies in Streaming Topic Models.” Conditionally accepted at Neurocomputing, 2023.
  3. Khang Nguyen, Kien Duc Do, Truong Tuan Vu, and Khoat Than. “Unsupervised Image Segmentation with Robust Virtual Class Contrast.” Pattern Recognition Letters, Volume 173, Pages 10-16, 2023.
  4. 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, Volume 35, Issue 4, pages 3742 – 3750, 2023.
  5. Chien Nguyen, Linh Ngo Van and Thien Huu Nguyen. “Retrieving Relevant Context to Align Representations for Cross-lingual Event Detection”. Findings of the Association for Computational Linguistics (ACL), 2023.
  6. Lam Tran, Viet Nguyen, Phi Nguyen, and Khoat Than. “Sharpness and Gradient Aware Minimization for Memory-based Continual Learning“. International Symposium on Information and Communication Technology (SOICT), 2023.
  7. Duy-Tung Nguyen, Duc-Manh Nguyen, Dinh-Tan Pham, Khoat Than, Hong-Thai Pham, Hai Vu. “Bayesian method for bee counting with noise-labeled data”. International Symposium on Information and Communication Technology (SOICT), 2023.
  8. Huy Huu Nguyen, Chien Van Nguyen, Linh Ngo Van, Luu Anh Tuan, Thien Huu Nguyen. “A Spectral Viewpoint on Continual Relation Extraction”. Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023

 

2022

  1. Ha Nguyen, Hoang Pham, Son Nguyen, Linh Ngo Van, Khoat Than. “Adaptive Infinite Dropout for Noisy and Sparse Data Streams,” Machine Learning journal, 111, pages 3025–3060, 2022.
  2. Tung Nguyen, Trung Mai, Nam Nguyen, Linh Ngo Van, Khoat Than. “Balancing stability and plasticity when learning topic models from short and noisy text streams”. Neurocomputing, Volume 505, Pages 30-43, 2022.
  3. 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, Vol 479, pp 89-105, 2022.
  4. Dieu Vu, Khang Truong, Khanh Nguyen, Linh Ngo, Khoat Than, “Revisiting Supervised Word Embeddings”, Journal of Information Science and Engineering, Vol. 38, pp 413-427, 2022.
  5. Linh Ngo, 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.
  6. 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. Lecture Notes in Computer Science. Springer, 2022.
  7. Hoang Phan, Anh Phan, 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. Lecture Notes in Computer Science. Springer, 2022.
  8. Son-Tung Tran, Van-Hung Le, Van-Nam Hoang, Khoat Than, Thanh-Hai Tran, Hai Vu, Thi-Lan Le. “A Local Structure-aware 3D Hand Pose Estimation Method for Egocentric Videos”. IEEE Ninth International Conference on Communications and Electronics (ICCE), 2022.
  9. Nghia Ngo Trung, Linh Ngo Van, and Thien Huu Nguyen. “Unsupervised Domain Adaptation for Text Classification via Meta Self-Paced Learning”, Proceedings of COLING 2022
  10. 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
  11. Viet Dac Lai, Hieu Man, Linh Ngo, Franck Dernoncourt and Thien Huu Nguyen. “Multilingual SubEvent Relation Extraction: A Novel Dataset and Structure Induction Method”. In Findings of the Association for Computational Linguistics: EMNLP 2022 (pp. 5559-5570).

 

2021

  1. Son Nguyen, Duong Nguyen, Khai Nguyen, Nhat Ho, Khoat Than, Hung Bui. “Structured Dropout Variational Inference for Bayesian Neural Networks”. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
  2. Bach, Tran Xuan, Nguyen Duc Anh, Linh Ngo Van, and Khoat Than. “Dynamic transformation of prior knowledge into Bayesian models for data streams.” Accepted, IEEE Transactions on Knowledge and Data Engineering, 2021.
  3. Nguyen, V.T., Le, T.T.K., Than, K., Tran, D.H. “Predicting miRNA–disease associations using improved random walk with restart and integrating multiple similarities.” Scientific Report, 11, 21071. Nature 2021.
  4. 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.
  5. Tuc Nguyen, Doanh Mai, Simon Su, Khoat Than. “An investigation of Graph Convolutional Networks for Collaborative Filtering: The role of nonlinear user-item prediction”. 2021.
  6. 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.
  7. T. -T. Ha, V. -N. Nguyen, K. -H. Nguyen, K. -A. Nguyen and Q. -K. Than, “Utilizing SBERT For Finding Similar Questions in Community Question Answering,” 2021 13th International Conference on Knowledge and Systems Engineering (KSE), 2021, pp. 1-6, doi: 10.1109/KSE53942.2021.9648830.

 

2020

  1. Shu, Rui, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, and Hung H. Bui. “Predictive Coding for Locally-Linear Control.” In Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.
  2. Linh Ngo, Bach Tran, and Khoat Than. “Graph Convolutional Topic Model for Data Streams.” arXiv preprint arXiv:2003.06112 (2020).
  3. Bach, Tran Xuan, Nguyen Duc Anh, Linh Ngo Van, and Khoat Than. “Dynamic transformation of prior knowledge into Bayesian models for data streams.” arXiv preprint arXiv:2003.06123 (2020).
  4. Xuan Bui, Hieu Vu, Oanh Nguyen, Khoat Than, “MAP estimation with Bernoulli randomness, and its application to text analysis and recommender systems,” IEEE Access, 2020.
  5. 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).
  6. N. Van Linh, D. A. Nguyen, T. B. Nguyen and K. Than, “Neural Poisson Factorization,” IEEE Access, doi: 10.1109/ACCESS.2020.2994239. 2020.
  7. Duong Minh Le, My T. Thai, Thien Huu Nguyen, “Multi-task Learning for Metaphor Detection with Graph Convolutional Neural Networks and Word Sense Disambiguation”, In Proceedings of Thirty-Fourth AAAI Conference on Artificial Intelligence, New York, USA, 2020.
  8. 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.

 

2019

  1. 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.
  2. 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.
  3. 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.
  4. Tuan Anh Phan, Nhat Nguyen Trong, Duong Bui, Linh Van Ngo, and Khoat Than, “From Implicit to Explicit Feedbacks: A deep neural network for modeling the sequential behaviors of online users”, In Proceeding of the Asian Conference on Machine Learning (ACML), 2019.
  5. Luong Nguyen Van and Oanh Nguyen Thi, “Object counting based on density using perspective transformation”, In Proceedings of RIVF. IEEE, 2019.
  6. Ba-Long Bui, Thi-Trang Nguyen, Huu-Hoang Nguyen and Kiem-Hieu Nguyen, “HMMs for Unsupervised Vietnamese Word Segmentation”, In Proceedings of RIVF. IEEE, 2019.
  7. Duong Nguyen, Kiem-Hieu Nguyen and Vi Ngo, “Neural sequence labeling for VietnamesePOS Tagging and NER”, In Proceedings of RIVF. IEEE, 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. Phuong Minh Nguyen, Khoat Than, Minh Le Nguyen. “Marking Mechanism in Sequence-to-sequence Model for Mapping Language to Logical Form”, In Proceedings of KSE. IEEE, 2019.

2018

  1. Hoa Le Minh, Son Ta Cong, Quyen Pham The, Linh Ngo Van, Khoat Than, “Collaborative Topic Model for Poisson distributed ratings”, International Journal of Approximate Reasoning, Volume 95, Pages 62-76, Springer, 2018.

  2. Huy Do, Khoat Than, Pierre Larmande, “Evaluating Named-Entity Recognition Approaches in Plant Molecular Biology”, In Proceedings of MIWAI. Lecture Notes in Artificial Intelligence, Springer, vol. 11248, pp. 219-225, 2018.

  3. Nguyen Trong Tung, Vu Hoang Dieu, Khoat Than, Ngo Van Linh, “Reducing Class Overlapping in Supervised Dimension Reduction”, In Proceedings of the Ninth International Symposium on Information and Communication Technology (SoICT). ACM, 2018.

  4. Xuan Bui, Tu Vu, Khoat Than, “Some Methods for Posterior Inference in Topic Models”, Research and Development on Information and Communication Technology Journal (RD-ICT), Vol. E-3. No. 15, 2018.

  5. Bui Thanh-Xuan, Vu Van-Tu, Atsuhiro Takasu, Khoat Than, “A fast algorithm for posterior inference with Latent Dirichlet Allocation”, In ACIIDS, Lecture Notes in Computer Science, Springer, 2018.
  6. Tu Vu, Xuan Bui, Khoat Than, Ryutaro Ichise, “A flexible stochastic method for solving the MAP problem in topic models”, Computacion y Sistemas journal, 2018.
  7. Huy Do, Hanh Tran, Khoat Than, Pierre Larmande, “Comparative study of Named-Entity Recognition methods in the agronomical domain”,  Computacion y Sistemas journal, 2018
  8. Kiem-Hieu Nguyen, “BKTreebank: Building a Vietnamese Dependency Treebank”. Proceedings of 11th Language Resources and Evaluation Conference, LREC 2018, Miyazaki, Japan.
  9. Van-Chung Vu, Thi-Thanh Ha, Kiem-Hieu Nguyen “Towards Event Timeline Generation from Vietnamese News”,  Proceedings of 19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018, Hanoi, Vietnam.
  10. Thi-Thanh Ha, Thanh-Chinh Nguyen, Kiem-Hieu Nguyen, Van-Chung Vu, Kim-Anh Nguyen, “Unsupervised Sentence Embeddings for Answer Summarization in non-factoid CQA”. Proceedings of 19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018, Hanoi, Vietnam.

2017

  1. Ngo Van Linh, Nguyen Kim Anh, Khoat Than, Chien Nguyen Dang, “An Effective and Interpretable Method for Document Classification”, Knowledge and Information Systems (KAIS), Volume 50, Issue 3, pp 763–793, 2017.
  2. Duc-Anh Nguyen, Kim Anh Nguyen, Linh Ngo, Khoat Than, “Keeping priors in streaming Bayesian learning”, Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science, vol 10234. Springer, 2017.
  3. Tung Doan and Khoat Than, “Sparse Stochastic Inference with Regularization”, Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science, vol 10234. Springer, 2017.
  4. Tran, Dang; Tran, Nhuan; Nguyen, Giang and Nguyen, Binh Minh. “A Proactive Cloud Scaling Model Based on Fuzzy Time Series and SLA Awareness”. In: Procedia Computer Science (International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland), p. 365-374, vol. 108, 2017, p. 365-374, ISSN: 1877-0509.

2016

  1. Nguyen Binh Minh; Tran Dang and Nguyen Giang, “Enhancing Service Capability with Multiple Finite-capacity Server Queues in Cloud Data Centers”,  Cluster Computing, 2016, vol. 19, issue 4, p. 1747-1767, ISSN 1386-7857.
  2. Hluchý, Ladislav; Nguyen, Giang; Astalos, Jan; Tran, Viet; Sipkova, Viera and Nguyen, Binh Minh. “Effective Computation Resilience in High Performance and Distributed Environments”. In: Computing and Informatics, 2016, vol. 35, no. 6, p. 1386-1415, ISSN 1335-9150.
  3. Cao Tien Dung; Hoang Huu Hanh; Huynh Xuan Hiep; Nguyen Binh Minh; Pham,Tran Vu; Tran Minh Quang; Tran The Vu and Truong Hong Linh, “IoT Services for Solving Critical Problems in Vietnam: A Research Landscape and Directions”,  IEEE Internet Computing, 2016, vol. 20, no.5, p.76-81, ISSN 1089-7801.
  4. LH Son, Hai V. Pham, “A novel multiple fuzzy clustering method based on internal clustering validation measures with gradient descent”, International Journal of Fuzzy Systems 18 (5), 894-903, 2016.
  5. Le Hoang Son, Pham Van Viet, Pham Van Hai; “Picture inference system: a new fuzzy inference system on picture fuzzy set”; APPLIED INTELLIGENCE, Vol. 46, pp. 652 – 669.
  6. TM Tuan, NT Duc, Hai V. Pham, LH Son, “Dental Diagnosis from X-Ray images using fuzzy rule-based systems”, International Journal of Fuzzy System Applications, Vol. 16, No. 1, pp. 1-16, 2017 (Scopus).
  7. Hoa M. Le, Thi-Oanh Nguyen, and Dung Ngo, “Fully automated multi-label image annotation by convolutional neural network and adaptive thresholding”,  In The Seventh International Symposium on Information and Communication Technology – SOICT 2016 (Best paper award).
  8. Duc-Cuong Dao, Thi-Oanh Nguyen, and Stephane Bressan,  “Factors influencing the performance of image captioning model: An evaluation”,  In The 14th International Conference on Advances in Mobile Computing and Multimedia, 2016.
  9. Huong T. Le, Son V. Nguyen, Lam N. Pham, Duy D. Nguyen and An N. Nguyen, “Semantic Text Alignment based on Topic Modeling”, In Proceeding of RIVF, IEEE, 2016 (Best runner-up paper award).
  10. Nguyen Binh Minh and Vu Tuyet Trinh, “An Approach to Integration and Interoperability in IoT Cloud Resources” In Proceeding of Regional Conference on Computer and Information Engineering 2016 (RCCIE), Yangon, 2016, p. 176-180, ISBN 978-99971-0-231-7.
  11. Tran Dang; Tran Nhuan; Nguyen Binh Minh and Le Hieu, “PD-GABP – A Novel Prediction Model Applying for Elastic Applications in Distributed Environment”, In Proceeding of 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS), IEEE, Danang, 2016, p. 241-246, ISBN: 978-1-5090-2100-0/16.
  12. Sipkova, Viera; Hluchý, Ladislav; Dobrucky, Miroslav; Bartok, Juraj and Nguyen, Binh Minh. “Manufacturing of Weather Forecasting Simulations on High Performance Infrastructures”. In proceeding of 12th International Conference on eScience (eScience), IEEE, Baltimore, 2016, p. 432-439, ISBN 978-1-5090-4272-2.
  13. Tran V.-T, Nguyen K.-H, Bui D.-H, “A Vietnamese Language Model Based on Recurrent Neural Network”, In the  eighth international conference on Knowledge and Systems Engineering (KSE), 2016.
  14. Tran H.-T, Nguyen T.-H, Tran V.-T, “Large-scale geographically weighted regression on Spark”, In the eighth international conference on Knowledge and Systems Engineering (KSE), 2016.
  15. Xuan Bui, Tu Vu, Khoat Than, “Stochastic bounds for inference in topic models”, In Proceeding of ICTA, Springer, 2016.
  16. Vu Le, Chien Phung, Cuong Vu, Ngo Van Linh, Khoat Than, “Streaming Sentiment-Aspect Analysis”, In Proceeding of RIVF, IEEE, 2016.
  17. Kiem-Hieu Nguyen, Xavier Tannier, Olivier Ferret and Romaric Besançon. A Dataset for Open Event Extraction in English. In Proceedings of 10th Language Resources and Evaluation Conference, LREC 2016.
  18. Khai Mai, Sang Mai, Anh Nguyen, Linh Ngo, Khoat Than, “Enabling Hierarchical Dirichlet Processes to work better for short texts at large scale”, In Proceedings of PAKDD. Lecture Notes in Computer Science, Springer, 2016.

2015

  1. Ngo Van Linh, Nguyen Kim Anh, Khoat Than, Nguyen Tat Nguyen, “Effective and Interpretable Document Classification using Distinctly Labeled Dirichlet Process Mixture Models of von Mises-Fisher Distributions”, In Proceedings of DASFAA. Lecture Notes in Computer Science, Springer, 2015.
  2. Linh Ngo Van, Anh Nguyen Kim and Khoat Than, “An effective NMF-based method for supervised dimension reduction”. In Proceedings of KSE. Advances in Intelligent Systems and Computing Volume 326, pages 93-104, 2015.
  3. Truong Hoang Linh, Bui Duy Khanh, Tran Viet Trung, “GPSInsights: Towards an efficient framework for storing and mining massive vehicle location data”, In Proceedings of the Sixth International Symposium on Information and Communication Technology. ACM, 2015.
  4. Nguyen, Binh Minh; Tran, Dang; Nguyen, Quynh, “A Strategy for Server Management to Improve Cloud Service QoS”. In Proceeding of 19th International Symposium on Distributed Simulation and Real Time Applications, IEEE/ACM, 2015.

2014

  1. Khoat Than, Tu Bao Ho, and Duy Khuong Nguyen, “An effective framework for supervised dimension reduction”. Neurocomputing, vol .139, pages 397-407, 2014.
  2. Khoat Than and Tung Doan, “Dual online inference for latent Dirichlet allocation”. In ACML. Journal of Machine Learning Research: W&CP, vol. 39, pages 80-95, 2014.
  3. Anh Nguyen Kim, Toi Nguyen Khac and Linh Ngo Van, “An Interpretable Method for Text Summarization Based on simplicial Non-negative Matrix Factorization”, In Proceeding of the the 4th Symposium on Information and Communication Technology  (SoICT 2014), Hanoi, Vietnam, 2014.