第一篇提出在神经网络中应用MoE的是在13年12月发表的Learning Factored Representations in a Deep Mixture of Experts[3]。 在这之前,MoE更多被使用在传统机器学习模型中,这篇文章提出一种新方法,可以在神经网络的每一层之上平行地拓展多个experts,每个expert有其各自的权重矩阵(结构一致,数值不同),然后由一个gati...
3.Sparsely-Gated Mixture-of-Experts3.1 专家路由Noisy Top-K Gating，摘自：Sparsely-Gated Mixture-o...
et al. Integrating contextual intelligence with mixture of experts for signature and anomaly-based intrusion detection in CPS security. Neural Comput & Applic 37, 5991–6007 (2025). https://doi.org/10.1007/s00521-024-10967-9 Download citation Received27 July 2024 Accepted23 December 2024 ...
OpenMoE is a project aimed at igniting the open-source MoE community! We are releasing a family of open-sourced Mixture-of-Experts (MoE) Large Language Models. Our project began in the summer of 2023. On August 22, 2023, we released the first batch of intermediate checkpoints (OpenMoE-ba...
LLaMA-MoE is a series of open-sourced Mixture-of-Expert (MoE) models based onLLaMAandSlimPajama. We build LLaMA-MoE with the following two steps: Partition LLaMA's FFNs into sparse experts and insert top-K gate for each layer of experts. ...
self._power_of_2 = (num_experts == 2**self._num_binary) if routing_input_shape is None: # z_logits is a trainable 3D tensor used for selecting the experts. # Axis 0: Number of non-zero experts to select. # Axis 1: Dummy axis of length 1 used for broadcasting. # Axis 2: Ea...
概括地讲,当下Sparsely-Gated Mixture of Experts的运行模式大致可以做如下解释: 将一个Transformer的部份FFN层(也可以是全部的),复制N份,用以代表N个不同的Experts,每个GPU上对应储存其中的一部份Experts; 在所有的Experts-FFN层之前,有一个Gating函数,用来负责每一个token往后的计算路径; ...
OpenMoE is a project aimed at igniting the open-source MoE community! We are releasing a family of open-sourced Mixture-of-Experts (MoE) Large Language Models. Our project began in the summer of 2023. On August 22, 2023, we released the first batch of intermediate checkpoints (OpenMoE-ba...
LLaMA-MoE is a series of open-sourced Mixture-of-Expert (MoE) models based onLLaMAandSlimPajama. We build LLaMA-MoE with the following two steps: Partition LLaMA's FFNs into sparse experts and insert top-K gate for each layer of experts. ...
Paper tables with annotated results for Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference