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Scaling Laws for Dense Retrieval

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

Tags

Information Retrieval Courses Machine Learning Courses Neural Networks Courses Scaling Laws Courses

Course Description

Overview

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Explore the latest research on scaling laws for dense retrieval in this 15-minute conference talk presented at SIGIR 2024. Delve into the findings of authors Yan Fang, Jingtao Zhan, Qingyao Ai, Jiaxin Mao, Weihang Su, Jia Chen, and Yiqun Liu as they discuss their work on Dense Retrieval 1 (T2.1). Gain insights into the scaling behaviors and performance improvements of dense retrieval models as they increase in size and complexity. Learn about the implications of these scaling laws for the development and optimization of information retrieval systems.

Syllabus

SIGIR 2024 T2.1 [fp] Scaling Laws For Dense Retrieval


Taught by

Association for Computing Machinery (ACM)

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