SOTAVerified

BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

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Showing 3926–3950 of 10033 papers

TitleStatusHype
DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data AnalysisCode0
Decoy Selection for Protein Structure Prediction Via Extreme Gradient Boosting and Ranking—0
Machine learning approach to force reconstruction in photoelastic materialsCode0
Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data—0
Machine-Learning the Sato--Tate Conjecture—0
Variance-Reduced Methods for Machine Learning—0
An Evaluation of Classification Methods for 3D Printing Time-Series Data—0
Explainable Online Validation of Machine Learning Models for Practical Applications—0
Neural BootstrapperCode1
Machine-learning-enhanced time-of-flight mass spectrometry analysis—0
Attention-Based Clustering: Learning a Kernel from ContextCode0
Nonsmoothness in Machine Learning: specific structure, proximal identification, and applications—0
Incorporating Machine Learning to Evaluate Solutions to the University Course Timetabling Problem—0
Deep Convolutional Transform Learning -- Extended version—0
The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications—0
A Machine Learning Framework for Computing the Most Probable Paths of Stochastic Dynamical Systems—0
Classifying Songs with EEG—0
A machine learning framework for LES closure terms—0
Machine learning on Crays to optimise petrophysical workflows in oil and gas exploration—0
TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos—0
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Momentum via Primal Averaging: Theoretical Insights and Learning Rate Schedules for Non-Convex OptimizationCode1
A Multi-modal Machine Learning Approach and Toolkit to Automate Recognition of Early Stages of Dementia among British Sign Language Users—0
When will the mist clear? On the Interpretability of Machine Learning for Medical Applications: a survey—0
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100—Unverified