1 code implementation • EMNLP (ArgMining) 2021 • Hoang Phan, Long Nguyen, Khanh Doan
Key Point Analysis (KPA) is one of the most essential tasks in building an Opinion Summarization system, which is capable of generating key points for a collection of arguments toward a particular topic.
no code implementations • 5 Mar 2024 • Hoang Phan, Andrew Gordon Wilson, Qi Lei
Models trained on data composed of different groups or domains can suffer from severe performance degradation under distribution shifts.
no code implementations • 16 Nov 2023 • Ngoc N. Tran, Lam Tran, Hoang Phan, Anh Bui, Tung Pham, Toan Tran, Dinh Phung, Trung Le
Contrastive learning (CL) is a self-supervised training paradigm that allows us to extract meaningful features without any label information.
no code implementations • 29 May 2023 • Boris Kovalerchuk, Hoang Phan
It is shown that this is a full machine learning approach that does not require processing n-dimensional data in an abstract n-dimensional space.
no code implementations • 17 May 2023 • Ngoc N. Tran, Son Duong, Hoang Phan, Tung Pham, Dinh Phung, Trung Le
Self-supervised learning aims to extract meaningful features from unlabeled data for further downstream tasks.
no code implementations • 30 Nov 2022 • Quyen Tran, Hoang Phan, Khoat Than, Dinh Phung, Trung Le
To address this issue, in this work, we first propose an online mixture model learning approach based on nice properties of the mature optimal transport theory (OT-MM).
no code implementations • 24 Nov 2022 • Hoang Phan, Lam Tran, Ngoc N. Tran, Nhat Ho, Dinh Phung, Trung Le
Multi-Task Learning (MTL) is a widely-used and powerful learning paradigm for training deep neural networks that allows learning more than one objective by a single backbone.
1 code implementation • 4 Jun 2022 • Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung
Furthermore, when analysing its asymptotic properties, SVGD reduces exactly to a single-objective optimization problem and can be viewed as a probabilistic version of this single-objective optimization problem.
no code implementations • Pacific-Asia Conference on Knowledge Discovery and Data Mining 2022 • Hoang Phan, Anh Phan Tuan, Son Nguyen, Ngo Van Linh, Khoat Than
Our paper studies the continual learning (CL) problems in which data comes in sequence and the trained models are expected to be capable of utilizing existing knowledge to solve new tasks without losing performance on previous ones.
1 code implementation • 1 Mar 2022 • Hoang Phan, Trung Le, Trung Phung, Tuan Anh Bui, Nhat Ho, Dinh Phung
First, they purely focus on local regularization to strengthen model robustness, missing a global regularization effect which is useful in many real-world applications (e. g., domain adaptation, domain generalization, and adversarial machine learning).
no code implementations • 14 Jun 2021 • Boris Kovalerchuk, Hoang Phan
It is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space.