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regression

Papers

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Showing 6776–6800 of 9424 papers

TitleStatusHype
Unsupervised Cross-Domain Regression for Fine-grained 3D Game Character Reconstruction—0
Unsupervised Domain Adaptation with Copula Models—0
Unsupervised Ensemble Regression—0
Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression—0
Unsupervised Functional Dependency Discovery for Data Preparation—0
Unsupervised Image Regression for Heterogeneous Change Detection—0
Unsupervised Learning For Effective User Engagement on Social Media—0
Unsupervised learning of regression mixture models with unknown number of components—0
Autoencoding Slow Representations for Semi-supervised Data Efficient Regression—0
UoR at SemEval-2021 Task 7: Utilizing Pre-trained DistilBERT Model and Multi-scale CNN for Humor Detection—0
UoR-NCL at SemEval-2022 Task 3: Fine-Tuning the BERT-Based Models for Validating Taxonomic Relations—0
uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network—0
Upper Counterfactual Confidence Bounds: a New Optimism Principle for Contextual Bandits—0
UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration—0
Detecting of multi-modality in probabilistic regression models—0
USAAR-SHEFFIELD: Semantic Textual Similarity with Deep Regression and Machine Translation Evaluation Metrics—0
Use Case Point Approach Based Software Effort Estimation using Various Support Vector Regression Kernel Methods—0
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution—0
Using 3D reconstruction from image motion to predict total leaf area in dwarf tomato plants—0
Using a Cross-Task Grid of Linear Probes to Interpret CNN Model Predictions On Retinal Images—0
A memory of motion for visual predictive control tasks—0
Using Analytic Scoring Rubrics in the Automatic Assessment of College-Level Summary Writing Tasks in L2—0
Using Artificial Intelligence to Improve Classroom Learning Experience—0
Using Contrastive Learning and Pseudolabels to learn representations for Retail Product Image Classification—0
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes—0
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All datasetsCalifornia Housing PricesConcrete Compressive StrengthMedical Cost Personal DatasetSynthetic: y = x * sin xADORECar_Price_Prediction

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.79—Unverified
2Baseline RegressionR2 Score0.58—Unverified
3Mimic / SurrogateR2 Score0.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.86—Unverified
2Baseline RegressionR2 Score0.59—Unverified
3Mimic / SurrogateR2 Score0.58—Unverified
#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.87—Unverified
2Baseline RegressionR2 Score0.78—Unverified
3Mimic / SurrogateR2 Score0.78—Unverified
#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.98—Unverified
2Baseline RegressionR2 Score-0.01—Unverified
3Mimic / SurrogateR2 Score-0.01—Unverified
#ModelMetricClaimedVerifiedStatus
1XGBoost-MACCSmicro-averaged RMSE0.93—Unverified
2RF-ToxPrintchemical macro-average RMSE0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1Linear and Decision Tree RegressionR Squared86.84—Unverified