Model card
RGTNet.
AcademicclassificationResidual Graph Transformer
FC-learned Residual Graph Transformer Network with Graph Encoder for temporal features. Graph Sparse Fitting with weighted aggregation. Published in Computer Methods and Programs in Biomedicine, Apr 2024.
§ 01 · Benchmarks
Every benchmark RGTNet has a recorded score for.
| # | Benchmark | Area · Task | Metric | Value | Rank | Date | Source |
|---|---|---|---|---|---|---|---|
| 01 | ABIDE I | Medical · Disease Classification | accuracy | 73.4% | #13 | — | source ↗ |
Rank column shows this model’s position vs all other models scored on the same benchmark + metric (competitors after the slash). #1 in red means current SOTA. Sorted by rank, then newest result.
§ 04 · Related models
Other Academic models scored on Codesota.
§ 05 · Sources & freshness
Where these numbers come from.
paper
1
result
1 of 1 rows marked verified.