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Binary Classification

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Showing 1451–1475 of 2574 papers

TitleStatusHype
Exploring Lexical, Syntactic, and Semantic Features for Chinese Textual Entailment in NTCIR RITE Evaluation Tasks—0
Exploring Pathological Speech Quality Assessment with ASR-Powered Wav2Vec2 in Data-Scarce Context—0
Exploring Sparsity in Multi-class Linear Discriminant Analysis—0
Exploring Strategies for Personalized Radiation Therapy Part I Unlocking Response-Related Tumor Subregions with Class Activation Mapping—0
Exploring the Feasibility of AI-Assisted Spine MRI Protocol Optimization Using DICOM Image Metadata—0
Exponential convergence of testing error for stochastic gradient methods—0
Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features—0
Extended and Unscented Gaussian Processes—0
Extracting PAC Decision Trees from Black Box Binary Classifiers: The Gender Bias Study Case on BERT-based Language Models—0
Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application—0
Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection—0
Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning—0
Eye Movements as Indicators of Deception: A Machine Learning Approach—0
Facial Forgery-based Deepfake Detection using Fine-Grained Features—0
Factor Importance Ranking and Selection using Total Indices—0
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning—0
Fair Classification via Unconstrained Optimization—0
Fair Decision Rules for Binary Classification—0
FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation—0
Fairness-aware Model-agnostic Positive and Unlabeled Learning—0
Fairness-Aware Naive Bayes Classifier for Data with Multiple Sensitive Features—0
Fairness by Explicability and Adversarial SHAP Learning—0
Fairness for Whom? Critically reframing fairness with Nash Welfare Product—0
Fairness in Machine Learning: A Survey—0
Fairness Through Regularization for Learning to Rank—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