1 citations · 1 across the 5 of their papers we have counts for
5 papers
InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs
Peiliang Gong, Emadeldeen Eldele, Chenyu Liu +8
Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting. However, existing methods predominantly rely on passive modality alignment…
Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
Peiliang Gong, Han Zhang, Zhen Jiang +5
Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled target domains without accessing…
Sequence Transferability and Task Order Selection in Continual Learning
Thinh Nguyen, Cuong N. Nguyen, Quang Pham +4
In continual learning, understanding the properties of task sequences and their relationships to model performance is important for developing advanced algorithms with better accur…
CompeteSMoE -- Effective Training of Sparse Mixture of Experts via Competition
Quang Pham, Giang Do, Huy Nguyen +8
Sparse mixture of experts (SMoE) offers an appealing solution to scale up the model complexity beyond the mean of increasing the network's depth or width. However, effective traini…
HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts
Giang Do, Khiem Le, Quang Pham +7
By routing input tokens to only a few split experts, Sparse Mixture-of-Experts has enabled efficient training of large language models. Recent findings suggest that fixing the rout…