SOTAVerified

BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

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Showing 2801–2825 of 10033 papers

TitleStatusHype
An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance—0
Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations—0
Machine Learning for Massive Industrial Internet of Things—0
BIKED: A Dataset for Computational Bicycle Design with Machine Learning BenchmarksCode1
Quantum machine learning with differential privacy—0
Interpretable Machine Learning: Moving From Mythos to Diagnostics—0
A critical reappraisal of predicting suicidal ideation using fMRICode0
Machine Learning Prediction of Time-Varying Rayleigh Channels—0
Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production SystemsCode1
Topology Applied to Machine Learning: From Global to Local—0
Mean-field methods and algorithmic perspectives for high-dimensional machine learning—0
Multi-Class Multiple Instance Learning for Predicting Precursors to Aviation Safety Events—0
Machine Learning Empowered Resource Allocation in IRS Aided MISO-NOMA Networks—0
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning DevicesCode2
Machine Learning the period finding algorithm—0
Exploring Coronal Heating Using Unsupervised Machine-Learning—0
Weather Analogs with a Machine Learning Similarity Metric for Renewable Resource Forecasting—0
Machine-learning based methodologies for 3d x-ray measurement, characterization and optimization for buried structures in advanced ic packages—0
On Ensemble LearningCode0
Entangled q-Convolutional Neural Nets—0
Machine Learning versus Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel—0
The Prevalence of Code Smells in Machine Learning projectsCode1
Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review—0
Eigen-spectrograms: An interpretable feature space for bearing fault diagnosis based on artificial intelligence and image processing—0
Autocalibration and Tweedie-dominance for Insurance Pricing with Machine LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100—Unverified