6 papers · 1 filter
On Bayesian Softmax-Gated Mixture-of-Experts Models
Nicola Bariletto, Huy Nguyen, Nhat Ho +1
Mixture-of-experts models provide a flexible framework for learning complex probabilistic input-output relationships by combining multiple expert models through an input-dependent…
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…
On Minimax Estimation of Parameters in Softmax-Contaminated Mixture of Experts
Fanqi Yan, Huy Nguyen, Dung Le +3
The softmax-contaminated mixture of experts (MoE) model is deployed when a large-scale pre-trained model, which plays the role of a fixed expert, is fine-tuned for learning downstr…
Convergence Rates for Softmax Gating Mixture of Experts
Huy Nguyen, Nhat Ho, Alessandro Rinaldo
Mixture of experts (MoE) has recently emerged as an effective framework to advance the efficiency and scalability of machine learning models by softly dividing complex tasks among…
Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts
Huy Nguyen, Nhat Ho, Alessandro Rinaldo
The softmax gating function is arguably the most popular choice in mixture of experts modeling. Despite its widespread use in practice, the softmax gating may lead to unnecessary c…
On Least Square Estimation in Softmax Gating Mixture of Experts
Huy Nguyen, Nhat Ho, Alessandro Rinaldo
Mixture of experts (MoE) model is a statistical machine learning design that aggregates multiple expert networks using a softmax gating function in order to form a more intricate a…