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24d6d158531508115e628188e2697f76-Supplemental-Datasets_and_Benchmarks.pdf
Next, we 6.1.2. Figure connected.
BeyondReal-worldBenchmarkDatasets: An EmpiricalStudyofNodeClassificationwithGNNs
Towards practical use cases of GNNs, researchers and developers need to deeply understand the strengths and weaknesses of GNNs from various aspects such as their accuracy, efficiency, and parameter sensitivity.
Appendix Disco
Algorithm CrossCo) gives S. ItfirstfullyS(line thecross-co (lines 10-12).
Latent Hierarchical Causal Structure Discoverywith Rank Constraints
Pa: Parents PCh: Purechildren PDe: Puredescendants Gp: Grandparents Sib: Siblings M: Measured puredescendants Table 1:Graphical notations.
Medical High-risk
T5 Foranypolic , states, andtreatmenta, if (s, a) 1+ Q D(s, a)and 2[0,1]exists thatPD(s, a)+ FD(s, a) , then (s, a) 1 . T6 Foranypolic , states, andtreata, if (s, a) Q R(s, a)and 2[0,1]exists PR(s, a)+ FR(s, a) , then (s, a) .
BalancedMeta-Softmax forLong-TailedVisualRecognition
Takinganinstance segmentation dataset, LVIS[7],forexample, the number of instances inbanana class can be thousands of times more than that of abait class.
SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models
Our SheetCopilot correctly completes 44.3% of tasks for
