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Binary Classification

Papers

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Showing 1701–1750 of 2574 papers

TitleStatusHype
Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?—0
Is Transductive Learning Equivalent to PAC Learning?—0
Is Your Model Sensitive? SPeDaC: A New Benchmark for Detecting and Classifying Sensitive Personal Data—0
Investigating Active Learning and Meta-Learning for Iterative Peptide Design—0
Iterative Rule Extension for Logic Analysis of Data: an MILP-based heuristic to derive interpretable binary classification from large datasets—0
It's Only Words And Words Are All I Have—0
JCT at SemEval-2020 Task 1: Combined Semantic Vector Spaces Models for Unsupervised Lexical Semantic Change Detection—0
JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Preprocessing Methods and various Machine Leaerning Methods—0
JCT at SemEval-2022 Task 6-A: Sarcasm Detection in Tweets Written in English and Arabic using Preprocessing Methods and Word N-grams—0
JCTDHS at SemEval-2019 Task 5: Detection of Hate Speech in Tweets using Deep Learning Methods, Character N-gram Features, and Preprocessing Methods—0
Joint empirical risk minimization for instance-dependent positive-unlabeled data—0
JUST-BLUE at SemEval-2021 Task 1: Predicting Lexical Complexity using BERT and RoBERTa Pre-trained Language Models—0
KaMiKla at SemEval-2022 Task 3: AlBERTo, BERT, and CamemBERT—Be(r)tween Taxonomy Detection and Prediction—0
Kernel-Free Universum Quadratic Surface Twin Support Vector Machines for Imbalanced Data—0
Kernel Support Vector Machine Classifiers with the _0-Norm Hinge Loss—0
KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling—0
KL-Divergence-Based Region Proposal Network for Object Detection—0
Knowledge-Guided Paraphrase Identification—0
Label Smoothing is Robustification against Model Misspecification—0
Language-aware Multiple Datasets Detection Pretraining for DETRs—0
Language-Based Automatic Assessment of Cognitive and Communicative Functions Related to Parkinson's Disease—0
Large Deviations for Classification Performance Analysis of Machine Learning Systems—0
Large Language Model Routing with Benchmark Datasets—0
Large Language Models for Healthcare Text Classification: A Systematic Review—0
Large Language Models For Text Classification: Case Study And Comprehensive Review—0
Large-Margin Classification with Multiple Decision Rules—0
Large-Margin Convex Polytope Machine—0
Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning—0
Large Neural Network Based Detection of Apnea, Bradycardia and Desaturation Events—0
Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification—0
LastResort at SemEval-2022 Task 4: Towards Patronizing and Condescending Language Detection using Pre-trained Transformer Based Models Ensembles—0
Latenrgy: Model Agnostic Latency and Energy Consumption Prediction for Binary Classifiers—0
LatentForensics: Towards frugal deepfake detection in the StyleGAN latent space—0
Learning a Descriptor-Specific 3D Keypoint Detector—0
Learning Affective Correspondence between Music and Image—0
Learning as MAP Inference in Discrete Graphical Models—0
Learning-based Bone Quality Classification Method for Spinal Metastasis—0
Learning CNN filters from user-drawn image markers for coconut-tree image classification—0
Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs—0
Learning Confidence Sets using Support Vector Machines—0
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision—0
Contrastive Video Representation Learning via Adversarial Perturbations—0
Learning Discriminative Video Representations Using Adversarial Perturbations—0
Learning Domain Invariant Representations for Child-Adult Classification from Speech—0
Learning-From-Disagreement: A Model Comparison and Visual Analytics Framework—0
Learning from few examples: Classifying sex from retinal images via deep learning—0
Learning from Imprecise and Fuzzy Observations: Data Disambiguation through Generalized Loss Minimization—0
Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation—0
Learning from Multiple Corrupted Sources, with Application to Learning from Label Proportions—0
Learning from Natural Language Explanations for Generalizable Entity Matching—0
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All datasetsfakekickstarterGlyphnet DatasetTII-SSRC-23

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