Name
Abstract | ||
|---|---|---|
We propose the Graph Space Embedding (GSE), a technique that maps the input into a space where interactions are implicitly encoded, with little computations required. We provide theoretical results on an optimal regime for the GSE, namely a feasibility region for its parameters, and demonstrate the experimental relevance of our findings. Next, we introduce a strategy to gain insight on which interactions are responsible for the certain predictions, paving the way for a far more transparent model. In an empirical evaluation on a real-world clinical cohort containing patients with suspected coronary artery disease, the GSE achieves far better performance than traditional algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.24963/ijcai.2019/451 | IJCAI |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| João Pereira | 1 | 2 | 1.74 |
| Albert K. Groen | 2 | 1 | 1.09 |
| Erik S. G. Stroes | 3 | 0 | 1.01 |
| Evgeni Levin | 4 | 0 | 0.68 |