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BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

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Showing 3201–3225 of 10033 papers

TitleStatusHype
Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments—0
Double/Debiased Machine Learning for Dynamic Treatment Effects—0
Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation—0
Compressive Classification (Machine Learning without learning)—0
De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers—0
Double/Debiased/Neyman Machine Learning of Treatment Effects—0
Automated EEG-based Screening of Depression Using Deep Convolutional Neural Network—0
Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale—0
Compression Implies Generalization—0
Applying Machine Learning Algorithms for Kidney Disease Diagnosis—0
A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems—0
Double machine learning for sample selection models—0
Evaluating (weighted) dynamic treatment effects by double machine learning—0
Comprehensive evaluation of no-reference image quality assessment algorithms on authentic distortions—0
Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models—0
Comprehensive Efficiency Analysis of Machine Learning Algorithms for Developing Hardware-Based Cybersecurity Countermeasures—0
Compositional Stochastic Average Gradient for Machine Learning and Related Applications—0
DP-ADMM: ADMM-based Distributed Learning with Differential Privacy—0
DPDR: A novel machine learning method for the Decision Process for Dimensionality Reduction—0
Automated identification of transiting exoplanet candidates in NASA Transiting Exoplanets Survey Satellite (TESS) data with machine learning methods—0
Automated Inference on Sociopsychological Impressions of Attractive Female Faces—0
DP-XGBoost: Private Machine Learning at Scale—0
DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering—0
Drawing Inspiration from Biological Dendrites to Empower Artificial Neural Networks—0
Stochastic Generalized Adversarial Label Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100—Unverified