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.
Dimension Importance Estimation for Dense Information Retrieval - Tutorial 2.1
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 T2.1 [fp] Dimension Importance Estimation for Dense Information Retrieval
Taught by
Association for Computing Machinery (ACM)
Related Courses
-
Optimizing Data Retrieval Techniques
-
Scaling Laws for Dense Retrieval
-
Introduction to Information Retrieval with Amazon Q
-
Graded Relevance Scoring of Written Essays with Dense Retrieval - Tutorial 2.1
-
Neural Passage Quality Estimation for Static Pruning - Efficiency for Search
-
Text Retrieval and Search Engines
3.2
