Dimension Importance Estimation for Dense Information Retrieval - Tutorial 2.1

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

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!

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)

Related Courses

Design and Analysis of Algorithms
Chennai Mathematical Institute via Swayam How to Win Coding Competitions: Secrets of Champions
ITMO University via edX Artificial Intelligence
Georgia Institute of Technology via Udacity Introdução à Ciência da Computação com Python Parte 2
Universidade de São Paulo via Coursera Introducción a la programación en Java: empezando a programar
Universidad Carlos iii de Madrid via edX