# Recurrent Entity Network

> memory-augmented neural network

**Wikidata**: [Q28015646](https://www.wikidata.org/wiki/Q28015646)  
**Source**: https://4ort.xyz/entity/recurrent-entity-network

## Summary
The Recurrent Entity Network is a memory-augmented neural network architecture introduced in 2016. It extends traditional recurrent neural networks by incorporating external memory components that can store and retrieve information about entities and their relationships over time.

## Key Facts
- Inception occurred in 2016
- Subclass of artificial neural network
- Incorporates external memory components beyond standard RNN hidden states
- Designed to handle sequential data with entity tracking capabilities
- Memory-augmented architecture enables longer-term information retention

## FAQs
### Q: What makes Recurrent Entity Network different from standard RNNs?
A: Recurrent Entity Networks incorporate external memory components that can store information about entities and their relationships, allowing them to maintain context over longer sequences than standard RNNs.

### Q: When was the Recurrent Entity Network introduced?
A: The Recurrent Entity Network was introduced in 2016 as a memory-augmented neural network architecture.

### Q: What type of neural network is a Recurrent Entity Network?
A: A Recurrent Entity Network is a subclass of artificial neural network that specifically falls under the category of memory-augmented neural networks.

## Why It Matters
Recurrent Entity Networks address fundamental limitations in traditional recurrent neural networks regarding long-term memory and entity tracking. Standard RNNs struggle to maintain information about entities and their relationships across extended sequences due to vanishing gradient problems and limited memory capacity. The memory-augmented architecture of Recurrent Entity Networks enables more effective processing of sequential data where maintaining context about multiple entities is crucial. This advancement has implications for natural language processing tasks, question answering systems, and other applications requiring persistent entity tracking. The architecture represents an evolution in neural network design that bridges the gap between computational efficiency and cognitive-inspired memory mechanisms.

## Notable For
- Integration of external memory components with recurrent neural network architecture
- Enhanced capability for tracking entities and relationships over time
- Memory-augmented design that surpasses traditional RNN limitations
- Application potential in complex sequence modeling tasks

## Body
### Architecture Design
The Recurrent Entity Network combines recurrent neural network components with external memory storage. The architecture maintains separate memory slots for different entities encountered during sequence processing. Information retrieval from memory occurs through attention mechanisms that focus on relevant entity representations.

### Memory Components
External memory stores entity-specific information separate from the core RNN hidden states. The memory system allows for selective reading and writing operations based on current input and previously stored entity information. Memory addressing follows content-based retrieval patterns that match query vectors to stored entity representations.

### Entity Tracking Mechanism
The network maintains distinct representations for different entities within the memory system. Entity-specific memory updates occur when new information about particular entities becomes available. Tracking persists across time steps through dedicated memory slot maintenance.

### Sequential Processing
Input sequences undergo processing where each step potentially updates entity representations in external memory. The combination of recurrent processing and memory augmentation enables complex reasoning about temporal relationships between entities.

## Schema Markup
```json
{
  "@context": "https://schema.org",
  "@type": "Thing",
  "name": "Recurrent Entity Network",
  "description": "A memory-augmented neural network architecture introduced in 2016 as a subclass of artificial neural networks.",
  "additionalType": "artificial neural network"
}