12 papers
A Statistical Theory of Gated Attention through the Lens of Hierarchical Mixture of Experts
Viet Nguyen, Tuan Minh Pham, Thinh Cao +4
Self-attention has greatly contributed to the success of the widely used Transformer architecture by enabling learning from data with long-range dependencies. In an effort to impro…
Rethinking Multinomial Logistic Mixture of Experts with Sigmoid Gating Function
Tuan Minh Pham, Thinh Cao, Viet Nguyen +3
The sigmoid gate in mixture-of-experts (MoE) models has been empirically shown to outperform the softmax gate across several tasks, ranging from approximating feed-forward networks…
Improving Minimax Estimation Rates for Contaminated Mixture of Multinomial Logistic Experts via Expert Heterogeneity
Fanqi Yan, Dung Le, Trang Pham +2
Contaminated mixture of experts (MoE) is motivated by transfer learning methods where a pre-trained model, acting as a frozen expert, is integrated with an adapter model, functioni…
DoRAN: Stabilizing Weight-Decomposed Low-Rank Adaptation via Noise Injection and Auxiliary Networks
Nghiem T. Diep, Hien Dang, Tuan Truong +3
Parameter-efficient fine-tuning (PEFT) methods have become the standard paradigm for adapting large-scale models. Among these techniques, Weight-Decomposed Low-Rank Adaptation (DoR…
Hypernetwork-Driven Low-Rank Adaptation Across Attention Heads
Nghiem T. Diep, Dung Le, Tuan Truong +3
Parameter-efficient fine-tuning (PEFT) has emerged as a powerful paradigm for adapting large-scale pre-trained models to downstream tasks with minimal additional parameters. Among…
CompeteSMoE -- Statistically Guaranteed Mixture of Experts Training via Competition
Nam V. Nguyen, Huy Nguyen, Quang Pham +3
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, we argue that ef…