Tim Kraska, MIT, The Case for Learned Index Structures

Stanford Seminar - The Case for Learned Index Structures

Systems That Learn: Panel Discussion

Niv Dayan, Harvard University, Dostoevsky: Better Space-Time Trade-Offs for LSM-Tree Based Key-Value

Huanch Zhang, CMU, SuRF: Practical Range Query Filtering with Fast Succinct Tries

An Introduction to Graph Neural Networks: Models and Applications

Learned Index Structure - Prof. Tim Kraska

1. Algorithms and Computation

Machine Learning in Databases Tutorial: Learned Indexes, Hash Tables, and B-Trees - Tyler Bettilyon

Designing for Understandability: The Raft Consensus Algorithm

XLDB-2018: Learned Index Structures

Deep Learning Day: Agents

Abstractions for Expressive, Efficient Parallel and Distributed Computing

F(by) 2017. Michael Snoyman - What Makes Haskell Unique.

Robert Binna, University of Innsbruck, HOT: A Height Optimized Trie Index for Main-Memory DBMS

12. Clustering

GraphRAG: союз графов знаний и RAG: Эмиль Эйфрем

CRDTs and the Quest for Distributed Consistency

Ziqi Wang, Carnegie-Mellon University, Building a Bw-Tree Takes More Than Just Buzz Words

7. Testing, Debugging, Exceptions, and Assertions

Samuel McCauley, BARC and IT University of Copenhagen, Set Similarity Search for Skewed Data