Summary of Learning Mixtures Of Experts with Em, by Quentin Fruytier et al.
Learning Mixtures of Experts with EM
by Quentin Fruytier, Aryan Mokhtari, Sujay Sanghavi
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the efficiency of the Expectation Maximization (EM) algorithm for training Mixtures of Experts (MoE) models. MoE models involve partitioning the input space with separate “expert” models trained on each partition, which have become popular components in large language models to reduce training and inference costs. The authors analyze EM for linear or logistic experts, showing it’s equivalent to Mirror Descent with a unit step size and Kullback-Leibler Divergence regularizer. This perspective allows deriving new convergence results and identifying local linear convergence conditions based on the signal-to-noise ratio (SNR). Experiments on synthetic and real-world data demonstrate EM outperforms gradient descent in terms of convergence rate and achieved accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoE models are a type of machine learning model that partition the input space, with separate “expert” models trained on each partition. These models have become popular in large language models to reduce training and inference costs. The paper looks at how well the Expectation Maximization (EM) algorithm works for training these models. The authors show that EM is a good way to train MoE models, especially when the experts are linear or logistic. This can help us understand more about how EM works and why it’s a good choice for training certain types of machine learning models. |
Keywords
» Artificial intelligence » Gradient descent » Inference » Machine learning
