Cookie Consent
Hi, this website uses essential cookies to ensure its proper operation and tracking cookies to understand how you interact with it. The latter will be set only after consent.
Read our Privacy Policy

Sparse Representation

Sparse representation in machine learning refers to a way of representing data or models where most of the elements are zero or near-zero, leading to a high degree of sparsity. This is particularly useful in scenarios where data naturally contains many zeroes or in situations where a more compact representation is beneficial.

How Sparse Representation Works

Consider a natural language processing task where a large vocabulary is used, but each document only contains a small subset of the words. Representing each document as a vector, most elements would be zero, indicating the absence of most words. This sparsity can be exploited for more efficient storage and faster computations.

Lakera LLM Security Playbook
Learn how to protect against the most common LLM vulnerabilities

Download this guide to delve into the most common LLM security risks and ways to mitigate them.

Related terms
untouchable mode.
Get started for free.

Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.

Join our Slack Community.

Several people are typing about AI/ML security. 
Come join us and 1000+ others in a chat that’s thoroughly SFW.