1 citations · 1 across the 6 of their papers we have counts for
7 papers
DWA-KD: Dual-Space Weighting and Time-Warped Alignment for Cross-Tokenizer Knowledge Distillation
Duc Trung Vu, Pham Khanh Chi, Dat Phi Van +3
Knowledge Distillation (KD) has emerged as a crucial technique for compressing Large Language Models (LLMs). Although existing cross-tokenizer KD methods have made notable progress…
XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments
Tien Phat Nguyen, Vu Minh Ngo, Tung Nguyen +4
Cross-lingual topic modeling aims to uncover shared semantic themes across languages. Several methods have been proposed to address this problem, leveraging both traditional and ne…
Few-shot Continual Relation Extraction via Open Information Extraction
Thiem Nguyen, Anh Nguyen, Quyen Tran +4
Typically, Few-shot Continual Relation Extraction (FCRE) models must balance retaining prior knowledge while adapting to new tasks with extremely limited data. However, real-world…
Leveraging Hierarchical Taxonomies in Prompt-based Continual Learning
Quyen Tran, Hoang Phan, Minh Le +6
Humans perceive the world as a series of sequential events, which can be hierarchically organized with different levels of abstraction based on conceptual knowledge. Drawing inspir…
Mixture of Experts Meets Prompt-Based Continual Learning
Minh Le, An Nguyen, Huy Nguyen +4
Exploiting the power of pre-trained models, prompt-based approaches stand out compared to other continual learning solutions in effectively preventing catastrophic forgetting, even…
Dynamic transformation of prior knowledge into Bayesian models for data streams
Tran Xuan Bach, Nguyen Duc Anh, Ngo Van Linh +1
We consider how to effectively use prior knowledge when learning a Bayesian model from streaming environments where the data come infinitely and sequentially. This problem is highl…
A Graph Convolutional Topic Model for Short and Noisy Text Streams
Ngo Van Linh, Tran Xuan Bach, Khoat Than
Learning hidden topics from data streams has become absolutely necessary but posed challenging problems such as concept drift as well as short and noisy data. Using prior knowledge…