Mixture of experts (MoE) is a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous...

expensive to train and use directly. For such models, mixture of experts (MoE) can be applied, a line of research pursued by Google researchers since 2017...

pioneering integration of the Mixture of Experts (MoE) technique with the Mamba architecture, enhancing the efficiency and scalability of State Space Models...

Mixtral 8x7B uses a sparse mixture of experts architecture. The model has 8 distinct groups of "experts", giving the model a total of 46.7B usable parameters...

architecture, a mixture-of-experts approach, and a larger one-million-token context window, which equates to roughly an hour of silent video, 11 hours of audio...

2024. It is a mixture-of-experts Transformer model, with 132 billion parameters in total. 36 billion parameters (4 out of 16 experts) are active for...

billion parameter model trained using a mixture-of-experts (MoE) technique with 12B active parameters (number of parameters active per token). Jamba can...

arises with the use of sparse models, such as mixture-of-expert models. In sparse models, during every inference, only a fraction of the parameters are...

learning restricted Boltzmann machines. Mixture of experts Boltzmann machine Hinton, G.E. (1999). "Products of experts". 9th International Conference on Artificial...

with the United States".. Notably, the type of architecture used for Wu Dao 2.0 is a mixture-of-experts (MoE) model, unlike GPT-3, which is a "dense"...