多门专家混合模型 (MMoE) 架构及广告参与建模开发中的知识蒸馏
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Authors: Jiacheng Li | Machine Learning Engineer II, Ads Ranking; Matt Meng | Staff Machine Learning Engineer, Ads Ranking; Kungang Li | Principal Machine Learning Engineer, Ads Performance; Qifei Shen | Senior Staff Machine Learning Engineer, Ads Ranking
作者：Jiacheng Li | 机器学习工程师 II，广告排名；Matt Meng | 高级机器学习工程师，广告排名；Kungang Li | 首席机器学习工程师，广告表现；Qifei Shen | 高级首席机器学习工程师，广告排名
Introduction
介绍
Multi-gate Mixture-of-Experts (MMoE)[1,2] is a recent industry-proven powerful architecture in neural network models that offers several significant benefits. First, it enhances model efficiency by dynamically allocating computational resources to different sub-networks (experts) based on the input data, ensuring that only the most relevant experts are activated for each task. This selective activation reduces computational overhead and improves inference speed. Second, MMoE promotes better generalization and performance by allowing the model to learn specialized features through multiple experts, each focusing on different aspects of the data. This specialization helps in capturing complex patterns and relationships that a single monolithic model might miss. Additionally, the multi-gate mechanism enables the model to handle multi-task learning more effectively, as it can tailor the contribution of each expert to different tasks, leading to improved accuracy and robustness across various applications. Overall, MMoE provides a flexible, efficient, and powerful approach to building advanced neural network models.
多门混合专家（MMoE）[1,2]是最近在神经网络模型中经过行业验证的强大架构，提供了几个显著的好处。首先，它通过根据输入数据动态分配计算资源给不同的子网络（专家），提高了模型效率，确保每个任务仅激活最相关的专家。这种选择性激活减少了计算开销，提高了推理速度。其次，MMoE通过允许模型通过多个专家学习专业特征，促进了更好的泛化和性能，每个专家专注于数据的不同方面。这种专业化有助于捕捉单一整体模型可能遗漏的复杂模式和关系。此外，多门机制使模型能够更有效地处理多任务学习，因为它可以根据不同任务调整每个专家的贡献，从而提高各种应用的准确性和鲁棒性。总体而言...