Dimension Importance Estimation for Dense Information Retrieval - Tutorial 2.1

Offered By: Association for Computing Machinery (ACM) via YouTube

Course Description

Overview

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Explore a focused conference talk on dimension importance estimation for dense information retrieval. Delve into the research presented by authors Guglielmo Faggioli, Nicola Ferro, Raffaele Perego, and Nicola Tonellotto as part of the Dense Retrieval 1 (T2.1) session at SIGIR 2024. Gain insights into advanced techniques and methodologies for improving dense retrieval systems through dimension importance estimation. Learn about the latest developments in this crucial aspect of information retrieval and its potential impact on search efficiency and effectiveness.

Syllabus

SIGIR 2024 T2.1 [fp] Dimension Importance Estimation for Dense Information Retrieval

Taught by

Association for Computing Machinery (ACM)

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