Graph Representation Learning
Graph Finite-State Automaton
Introduced by Johnson et al. in Learning Graph Structure With A Finite-State Automaton LayerGraph Finite-State Automaton, or GFSA, is a differentiable layer for learning graph structure that adds a new edge type (expressed as a weighted adjacency matrix) to a base graph. This layer can be trained end-to-end to add derived relationships (edges) to arbitrary graph-structured data based on performance on a downstream task.
Source: Learning Graph Structure With A Finite-State Automaton Layer
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Code Classification | 1 | 33.33% |
| Speech Recognition | 1 | 33.33% |
| Variable misuse | 1 | 33.33% |
Usage Over Time
Components
| Component | Type |
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|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
