Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Dimension Importance Estimation for Dense Information Retrieval - Tutorial 2.1

Association for Computing Machinery (ACM) via YouTube

Overview

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)

Reviews

Start your review of Dimension Importance Estimation for Dense Information Retrieval - Tutorial 2.1

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.