To help the open-source community have a better understanding of Mixture-of-Experts
(MoE) based large language models (LLMs), we train and release OpenMoE, a series of …

As machine learning models in critical fields increasingly grapple with multimodal data, they
face the dual challenges of handling a wide array of modalities, often incomplete due to …

Top-K sparse softmax gating mixture of experts has been widely used for scaling up massive
deep-learning architectures without increasing the computational cost. Despite its popularity …

Dense-to-sparse gating mixture of experts (MoE) has recently become an effective
alternative to a well-known sparse MoE. Rather than fixing the number of activated experts …

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 and …

Transformer based large language models have achieved tremendous success. However,
the significant memory and computational costs incurred during the inference process make …

Mixture-of-experts (MoE) model incorporates the power of multiple submodels via gating
functions to achieve greater performance in numerous regression and classification …

Abstract Machine intelligence is at great height these days and has been evident with its
effective provenance in almost all domains of science and technology. This work will focus …

Deep learning models, despite their potential for increasing our understanding of intricate
neuroimaging data, can be hampered by challenges related to interpretability. Multimodal …

The softmax gating function is arguably the most popular choice in mixture of experts
modeling. Despite its widespread use in practice, softmax gating may lead to unnecessary …