SOTAVerified

Ordinal Classification

Papers

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Showing 26–50 of 72 papers

TitleStatusHype
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
Label Noise Filtering Techniques to Improve Monotonic Classification—0
LAMPO: Large Language Models as Preference Machines for Few-shot Ordinal Classification—0
Mental Fatigue Monitoring using Brain Dynamics Preferences—0
Multimodal Assessment of Classroom Discourse Quality: A Text-Centered Attention-Based Multi-Task Learning Approach—0
Noncrossing Ordinal Classification—0
OCAPIS: R package for Ordinal Classification And Preprocessing In Scala—0
Optimizing Automatic Speech Assessment: W-RankSim Regularization and Hybrid Feature Fusion Strategies—0
Prostate Tissue Grading with Deep Quantum Measurement Ordinal Regression—0
Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation—0
Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models—0
SDNet: Semantically Guided Depth Estimation Network—0
SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets—0
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
YNU-HPCC at SemEval-2020 Task 10: Using a Multi-granularity Ordinal Classification of the BiLSTM Model for Emphasis Selection—0
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Benchmark Results

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