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regression

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Showing 2951–2975 of 9424 papers

TitleStatusHype
Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax optimality—0
Embryonic Elongation controlled by Graded Cell Fate: A Molecular Dynamics Approach—0
EM Converges for a Mixture of Many Linear Regressions—0
Emerging Relation Network and Task Embedding for Multi-Task Regression Problems—0
EM for Mixture of Linear Regression with Clustered Data—0
EMLight: Lighting Estimation via Spherical Distribution Approximation—0
A New Test for Market Efficiency and Uncovered Interest Parity—0
Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification—0
A Unified Scheme of ResNet and Softmax—0
EmoNLP at SemEval-2018 Task 2: English Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional LSTM—0
A machine learning approach to the prediction of heat-transfer coefficients in micro-channels—0
Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data—0
Bosonic Random Walk Networks for Graph Learning—0
Emotion Recognition Using Convolutional Neural Networks—0
Empathy and Distress Prediction using Transformer Multi-output Regression and Emotion Analysis with an Ensemble of Supervised and Zero-Shot Learning Models—0
EMPHASIS: An Emotional Phoneme-based Acoustic Model for Speech Synthesis System—0
Adaptive observer of state variables of a nonlinear time varying system with unknown constant parameters—0
Empirical Bayes Estimation for Lasso-Type Regularizers: Analysis of Automatic Relevance Determination—0
Empirical Decomposition of the IV-OLS Gap with Heterogeneous and Nonlinear Effects—0
Empirical entropy, minimax regret and minimax risk—0
Deep Learning for Vertex Reconstruction of Neutrino-Nucleus Interaction Events with Combined Energy and Time Data—0
Empirical Models of the Time Evolution of SPX Option Prices—0
Regression-based Physics Informed Neural Networks (Reg-PINNs) for Magnetopause Tracking—0
Empirical risk minimization is consistent with the mean absolute percentage error—0
A Unified Framework for Training Neural Networks—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