Itinai.com llm large language model graph clusters multidimen a773780d 551d 4815 a14e 67b061d03da9 2
Itinai.com llm large language model graph clusters multidimen a773780d 551d 4815 a14e 67b061d03da9 2

Memory-Efficient Embeddings

The text discusses the challenges of using one-hot encoding for handling large categorical data and introduces a solution through the use of embeddings, addressing memory requirements and computational complexity. It details methods for reducing memory footprint, including dimension reduction, hashing, and the quotient-remainder trick, as well as their implementation in TensorFlow. The author also shares their experiments and results, emphasizing the effectiveness of various methods.

 Memory-Efficient Embeddings

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Memory-Efficient Embeddings

Memory-Efficient Embeddings

Creating smaller models with a new kind of embedding layer

Embeddings

Embeddings are real-valued vectors of the categories that are short and should capture some meaning of the categories. The real numbers are learned during training.

Model size

Embeddings are great, but if you have a lot of categories, you have to store a bunch of real numbers for each of them. The size of this table can get out of hand very easily.

Simple Tricks to Reduce the Embedding Table Size

There are several ways to make this table smaller but usually, there is a trade-off. Reducing the table size reduces your model’s performance.

Compositional Embeddings

Let us now turn to the paper Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems by Shi et al. In their paper, they describe a method to save memory that is better than the hashing trick.

Implementation in TensorFlow

Before we implement this logic in TensorFlow, let us all get on the same page. Let us recap how you can use embedding layers in TensorFlow:

Performance Results

The authors used the Criteo Ad Kaggle Competition dataset for click-through rate prediction to test their model. It has 13 dense features and 26 categorical features. They used two different neural network architectures to test their method: DCM and DLRM.

My experiments

I also played around with it by building a simple recommender for the MovieLens 20M dataset. This dataset is full of movie ratings by users in the form of a star rating from 1 to 5.

Conclusion & Discussion

In this article, we have seen that embeddings are a neat tool to encode categorical data. However, these embeddings can result in huge models that can be hard to deploy, so it is good to know measures to reduce the embedding table size.

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Vladimir Dyachkov, Ph.D
Editor-in-Chief itinai.com

I believe that AI is only as powerful as the human insight guiding it.

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