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20222025
most citedMixture of Experts Meets Prompt-Based Continual Learning

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2025

Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective

Minh Le, Tien Ngoc Luu, An Nguyen The +5

To address catastrophic forgetting in Continual Relation Extraction (CRE), many current approaches rely on memory buffers to rehearse previously learned knowledge while acquiring n…

cs.LG2024

Attack On Prompt: Backdoor Attack in Prompt-Based Continual Learning

Trang Nguyen, Anh Tran, Nhat Ho

Prompt-based approaches offer a cutting-edge solution to data privacy issues in continual learning, particularly in scenarios involving multiple data suppliers where long-term stor…

stat.ML2024

Statistical Advantages of Perturbing Cosine Router in Mixture of Experts

Huy Nguyen, Pedram Akbarian, Trang Pham +3

The cosine router in Mixture of Experts (MoE) has recently emerged as an attractive alternative to the conventional linear router. Indeed, the cosine router demonstrates favorable…

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…

stat.ML2022

Fast Approximation of the Generalized Sliced-Wasserstein Distance

Dung Le, Huy Nguyen, Khai Nguyen +2

Generalized sliced Wasserstein distance is a variant of sliced Wasserstein distance that exploits the power of non-linear projection through a given defining function to better cap…