SOTAVerified

Ordinal Classification

Papers

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Showing 26–50 of 72 papers

TitleStatusHype
SemEval-2018 Task 1: Affect in Tweets—0
SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection—0
Stay on Topic, Please: Aligning User Comments to the Content of a News Article—0
Supervised Contrastive Learning for Ordinal Engagement Measurement—0
Trust Modeling in Counseling Conversations: A Benchmark Study—0
UIUC at SemEval-2018 Task 1: Recognizing Affect with Ensemble Models—0
Unimodal probability distributions for deep ordinal classification—0
UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets—0
WER-BERT: Automatic WER Estimation with BERT in a Balanced Ordinal Classification Paradigm—0
YNU-HPCC at SemEval-2018 Task 1: BiLSTM with Attention based Sentiment Analysis for Affect in Tweets—0
Deep Sensor Fusion for Real-Time Odometry Estimation—0
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data—0
DIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Images—0
Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport—0
EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets—0
Electre Tri-Machine Learning Approach to the Record Linkage Problem—0
Evaluating Evaluation Measures for Ordinal Classification and Ordinal Quantification—0
Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques—0
From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking—0
Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification—0
Generative Regression Based Watch Time Prediction for Short-Video Recommendation—0
Image Ordinal Classification and Understanding: Grid Dropout with Masking Label—0
Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification—0
Improving the classification of extreme classes by means of loss regularisation and generalised beta distributions—0
A Supervised Machine Learning Model For Imputing Missing Boarding Stops In Smart Card Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet-18Mean absolute error2.56—Unverified