Name
Abstract | ||
|---|---|---|
We present Linear Relational Embedding (LRE), a new method of learning a distributed representation of concepts from data consisting of instances of relations between given concepts. Its final goal is to be able to generalize, i.e. infer new instances of these relations among the concepts. On a task involving family relationships we show that LRE can generalize better than any previously published method. We then show how LRE can be used effectively to find compact distributed representations for variable-sized recursive data structures, such as trees and lists. |
Year | Venue | Keywords |
|---|---|---|
2001 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 14, VOLS 1 AND 2 | recursive data structure,data consistency |
Field | DocType | Volume |
Data structure,Embedding,Computer science,Theoretical computer science,Artificial intelligence,Distributed representation,Machine learning,Recursion | Conference | 14 |
ISSN | Citations | PageRank |
1049-5258 | 9 | 0.83 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alberto Paccanaro | 1 | 206 | 24.14 |
| geoffrey e hinton | 2 | 40435 | 4751.69 |