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20202026
most citedMixture of Experts Meets Prompt-Based Continual Learning

1 citations · 1 across the 6 of their papers we have counts for

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cs.LG2025

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…

cs.LG2024

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…

cs.LG2024★ 1 cited

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…

cs.LG2020

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…

cs.LG2020

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…