Recurrent Neural Networks
Legendre Memory Unit
Introduced by Voelker et al. in Legendre Memory Units: Continuous-Time Representation in Recurrent Neural NetworksThe Legendre Memory Unit (LMU) is mathematically derived to orthogonalize its continuous-time history – doing so by solving d coupled ordinary differential equations (ODEs), whose phase space linearly maps onto sliding windows of time via the Legendre polynomials up to degree d-1. It is optimal for compressing temporal information.
See paper for equations (markdown isn't working).
Official github repo: https://github.com/abr/lmu
Source: Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 2 | 10.53% |
| Machine Translation | 2 | 10.53% |
| Sentiment Analysis | 2 | 10.53% |
| Sequential Image Classification | 2 | 10.53% |
| Translation | 2 | 10.53% |
| Fraud Detection | 1 | 5.26% |
| Speech Recognition | 1 | 5.26% |
| Natural Language Inference | 1 | 5.26% |
| Semantic Similarity | 1 | 5.26% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
