Search Results for author: Hoang Phan

Found 11 papers, 3 papers with code

Matching The Statements: A Simple and Accurate Model for Key Point Analysis

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.

Opinion Mining Opinion Summarization

Controllable Prompt Tuning For Balancing Group Distributional Robustness

no code implementations5 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.

Robust Contrastive Learning With Theory Guarantee

no code implementations16 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.

Contrastive Learning

Full High-Dimensional Intelligible Learning In 2-D Lossless Visualization Space

no code implementations29 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.

Classification

Sharpness & Shift-Aware Self-Supervised Learning

no code implementations17 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.

Classification Contrastive Learning +2

Continual Learning with Optimal Transport based Mixture Model

no code implementations30 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).

Class Incremental Learning Incremental Learning

Improving Multi-task Learning via Seeking Task-based Flat Regions

no code implementations24 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.

Multi-Task Learning speech-recognition +1

Stochastic Multiple Target Sampling Gradient Descent

1 code implementation4 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.

Multi-Task Learning

Reducing Catastrophic Forgetting in Neural Networks via Gaussian Mixture Approximation

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.

Computational Efficiency Continual Learning +1

Global-Local Regularization Via Distributional Robustness

1 code implementation1 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).

Domain Generalization

Full interpretable machine learning in 2D with inline coordinates

no code implementations14 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.

BIG-bench Machine Learning Interpretable Machine Learning

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