Explore a 15-minute conference talk on efficient inverted indexes for approximate retrieval over learned sparse representations. Delve into cutting-edge research presented by authors Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli, and Rossano Venturini at the SIGIR 2024 conference. Gain insights into advanced techniques for improving search efficiency, focusing on the application of learned sparse representations in information retrieval systems. Learn about innovative approaches to optimizing inverted indexes for faster and more accurate approximate retrieval, potentially revolutionizing search algorithms and their performance in various applications.
Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 M1.3 [fp] Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Rep
Taught by
Association for Computing Machinery (ACM)
Related Courses
-
GUITAR: Gradient Pruning toward Fast Neural Ranking - Efficiency for Search
-
Neural Passage Quality Estimation for Static Pruning - Efficiency for Search
-
Combining Inverted and ANN Indexes for Efficient Hybrid Search at Scale
-
Revisiting Document Expansion and Filtering for Effective First-Stage Retrieval - Lecture 3
-
Generative Retrieval as Multi-Vector Dense Retrieval
-
Generative Retrieval via Term Set Generation - The Future of Search with LLMs
