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Binary Classification

Papers

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Showing 2351–2375 of 2574 papers

TitleStatusHype
Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities—0
Convergence of Uncertainty Sampling for Active Learning—0
Convergence Properties of Deep Neural Networks on Separable Data—0
Convergence Rates for Empirical Estimation of Binary Classification Bounds—0
Convergence Rates of Active Learning for Maximum Likelihood Estimation—0
Convergence rates of sub-sampled Newton methods—0
Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy—0
Convex Fairness Constrained Model Using Causal Effect Estimators—0
Convexifying Sparse Interpolation with Infinitely Wide Neural Networks: An Atomic Norm Approach—0
Convolutional Neural Networks for Epileptic Seizure Prediction—0
Convolutional neural networks model improvements using demographics and image processing filters on chest x-rays—0
Co-occurring Diseases Heavily Influence the Performance of Weakly Supervised Learning Models for Classification of Chest CT—0
cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations—0
Copiloting Diagnosis of Autism in Real Clinical Scenarios via LLMs—0
Coronary Artery Disease Classification with Different Lesion Degree Ranges based on Deep Learning—0
Correlated Feature Aggregation by Region Helps Distinguish Aggressive from Indolent Clear Cell Renal Cell Carcinoma Subtypes on CT—0
Corruption Robust Active Learning—0
Cost Sensitive Learning in the Presence of Symmetric Label Noise—0
Counterfactual Explanations via Latent Space Projection and Interpolation—0
COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Images—0
COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19—0
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks—0
Critical Review for One-class Classification: recent advances and the reality behind them—0
Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples—0
Cross-Entropy Loss Leads To Poor Margins—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