SOTAVerified

Ordinal Classification

Papers

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Showing 51–72 of 72 papers

TitleStatusHype
A Dictionary-based approach to Time Series Ordinal Classification—0
Affect-driven Ordinal Engagement Measurement from Video—0
AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality—0
Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification—0
A Novel Machine Learning Approach to Data Inconsistency with respect to a Fuzzy Relation—0
A Novel Variational Autoencoder with Applications to Generative Modelling, Classification, and Ordinal Regression—0
a theoretical look at ordinal classification methods based on comparing actions with limiting boundaries between adjacent classes—0
A theoretical look at ordinal classification methods based on reference sets composed of characteristic actions—0
Automatic Estimation of Ulcerative Colitis Severity from Endoscopy Videos using Ordinal Multi-Instance Learning—0
CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Loss—0
Distribution-free Conformal Prediction for Ordinal Classification—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
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Benchmark Results

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