Mixture of Experts-Introduction

Abdulkader Helwan
11 min readAug 10, 2023

Mixture of Experts (MoE) is like a teamwork technique in the world of neural networks. Imagine breaking down a big task into smaller parts and having different experts tackle each part. Then, there’s a clever judge who decides which expert’s advice to follow based on the situation, and all these suggestions are blended.

Although it was first explained using nerdy neural network stuff, you can use this idea with any type of expert or model. It’s a bit like when you combine different flavors to make a tasty dish, and this belongs to the cool group of ensemble learning methods called meta-learning.

So, in this guide, you’ll get to know the mixture of experts trick for teaming up models.

Once you’re through with this guide, you’ll have a handle on:

  • How a smart way to work together involves dividing tasks and letting experts handle each part.
  • Mixture of experts is a cool method that tries to solve prediction problems by thinking about smaller tasks and expert models.
  • This idea of breaking things down and building up connects to decision trees, and the meta-learner concept is kind of like the super-stacker in the ensemble world.

Subtasks and Experts

Let’s break it down a bit further: sometimes tasks in the world of prediction can get pretty complex, but the cool thing is, they can often be split into smaller pieces that make more sense.

Imagine you’re trying to understand a wiggly line on a graph that looks like the letter “S”. Instead of trying to figure out the whole thing at once, you could be smart and chop it into three parts: the top wiggly part, the bottom wiggly part, and the straight line in the middle.

This way of solving problems is kind of like taking things one step at a time, and it’s used in all sorts of clever computer techniques for predicting stuff and problem-solving in general.

This approach also forms the basis for creating a special teamwork technique called ensemble learning.

Here’s how it works: let’s say you’re dealing with a tricky puzzle. You can split the puzzle pieces into different groups based on what you already know about the problem. Then, you train a smarty-pants model on each group, so they become experts on their specific part of the puzzle. These experts can then team up and help predict new stuff in the future.