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Binary Classification

Papers

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Showing 1376–1400 of 2574 papers

TitleStatusHype
PiCkLe at SemEval-2022 Task 4: Boosting Pre-trained Language Models with Task Specific Metadata and Cost Sensitive Learning—0
PINGAN Omini-Sinitic at SemEval-2022 Task 4: Multi-prompt Training for Patronizing and Condescending Language Detection—0
Pointwise Binary Classification with Pairwise Confidence Comparisons—0
Pointwise Paraphrase Appraisal is Potentially Problematic—0
Policy Contrastive Imitation Learning—0
Population Template-Based Brain Graph Augmentation for Improving One-Shot Learning Classification—0
Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error—0
Post-Hoc Calibrated Anomaly Detection—0
Post-selection Inference for Conformal Prediction: Trading off Coverage for Precision—0
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator—0
PowerGraph: A power grid benchmark dataset for graph neural networks—0
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training—0
Practical Perspectives on Quality Estimation for Machine Translation—0
Precise Statistical Analysis of Classification Accuracies for Adversarial Training—0
PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Prediction—0
Predicting Adolescent Suicide Attempts with Neural Networks—0
Predicting BVD Re-emergence in Irish Cattle From Highly Imbalanced Herd-Level Data Using Machine Learning Algorithms—0
Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach—0
Predicting Clickbait Strength in Online Social Media—0
Predicting Customer Churn in World of Warcraft—0
Predicting Defects in Laser Powder Bed Fusion using in-situ Thermal Imaging Data and Machine Learning—0
Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks—0
Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation—0
Predicting Ki67, ER, PR, and HER2 Statuses from H&E-stained Breast Cancer Images—0
Predicting Loss Risks for B2B Tendering Processes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Trompt + OpenAI embeddingAUROC0.98—Unverified
2LightGBM + OpenAI embeddingAUROC0.97—Unverified
3FTTransformer + RoBERTa fintuneAUROC0.96—Unverified
4LightGBM + RoBERTa embeddingAUROC0.95—Unverified
5FTTransformer + RoBERTa embeddingAUROC0.94—Unverified
6ResNet + RoBERTa embeddingAUROC0.93—Unverified
7ResNet + OpenAI embeddingAUROC0.92—Unverified
8FTTransformer + OpenAI embeddingAUROC0.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Trompt + OpenAI embeddingAUROC0.81—Unverified
2Multimodal-Net All-TextAUROC0.8—Unverified
3ResNet + RoBERTa finetuneAUROC0.79—Unverified
4LightGBM + RoBERTa embeddingAUROC0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1Attention based CNNF1 score0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1XGBoostF1-Score98.79—Unverified