paper_id stringlengths 10 10 | items listlengths 4 82 | gt float64 1 9.5 |
|---|---|---|
8FJ6MOiP91 | [
"strength: The paper proposes a simple method to handle imbalanced dataset for regression tasks. The method is straight forward and easy to implement.",
"strength: The idea of switching between loss functions during training appears novel, at least in the context of imbalanced regression. The proposed method is s... | 3 |
N1TyUhkvjW | [
"strength: Significant value in improving anomaly detection methods by \"small\" modifications in current methodologies",
"strength: Experimental results support the claims of the paper",
"strength: Technical sound ideas",
"strength: In particular, in unsupervised anomaly detection, a motif to overcome the li... | 5 |
00SnKBGTsz | [
"strength: Tackle a timely and interesting problem.",
"strength: Provide the necessary infrastructure for the community to study the problem, opening up opportunities for future contributions.",
"strength: Consider various data generation strategies,",
"strength: Well-desgined experiments which demonstrate th... | 7.5 |
cya3eEczAx | [
"strength: The topic of the paper is interesting.",
"strength: The paper provides a review of related works.",
"strength: The proposed work is interesting and aims to tackle a well-known issue arising in the non-differentiability of the loss function in Predict & Optimize.",
"strength: The numerical experimen... | 1.666667 |
FtX6oAW7Dd | [
"strength: The paper is well-written and accessible.",
"strength: Model selection in PLL is a relevant, underexplored topic.",
"strength: The proposed dataset in a realistic PLL setting adds significant value to the PLL literature.",
"strength: Despite the model selection criteria's simplicity, the authors es... | 7.5 |
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