NIPS
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Exercise Cold Start | NIPS34 | Accuracy70.76 | 27 | |
| Cognitive Diagnosis | NIPS34 Geometry as Target | Accuracy71.8 | 18 | |
| Cognitive Diagnosis | NIPS34 Algebra as Target | Accuracy71.16 | 18 | |
| Cognitive Diagnosis | NIPS Number as Target 34 | Accuracy72.23 | 18 | |
| Knowledge Tracing | NIPS34 | AUC80.36 | 15 | |
| Adversarial Attack Detection | NIPS M-Attack in-domain 17 | Precision100 | 10 | |
| Adversarial Detection | NIPS to Medical cross-domain 17 | Precision (SSA-CWA)98.9 | 10 | |
| Density Estimation | NIPS-0-12 UCI repository (test) | Avg Log-Likelihood (nats)-272.38 | 9 | |
| Distribution Estimation | NIPS-0-12 (test) | Negative Log-Likelihood272.39 | 8 | |
| SSA-CWA to FOA-Attack Cross-Attack Detection | NIPS 17 | Precision100 | 6 | |
| SSA-CWA to M-Attack Cross-Attack Detection | NIPS 17 | Precision100 | 6 | |
| Matrix Completion | NIPS (test) | RMSE0.04 | 6 | |
| Targeted Adversarial Attack (Victim: Claude 3.7) | NIPS 2017 | ASR18.5 | 5 | |
| Targeted Adversarial Attack (Victim: GPT-4o) | NIPS 2017 | ASR86.5 | 5 | |
| Adversarial Attack Detection | NIPS FOA-Attack in-domain 17 | Precision100 | 5 | |
| Adversarial Attack Detection | NIPS SSA-CWA in-domain 17 | Precision100 | 5 | |
| Instance Segmentation | NIPS in-domain (test) | Accuracy90.91 | 5 | |
| Runtime Evaluation | nips | Runtime (s)0.0783 | 5 | |
| Runtime Efficiency | nips 25% query size | Runtime (s)0.0752 | 5 | |
| MAP Inference | nips 50% query size | Runtime (s)0.0764 | 5 |
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