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BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7226–7250 of 10033 papers

TitleStatusHype
Minerva: A Portable Machine Learning Microservice Framework for Traditional Enterprise SaaS Applications—0
Minimax deviation strategies for machine learning and recognition with short learning samples—0
Minimax Distribution Estimation in Wasserstein Distance—0
Minimax Lower Bounds for Cost Sensitive Classification—0
Minimax Regret Optimization for Robust Machine Learning under Distribution Shift—0
Minimizing the Societal Cost of Credit Card Fraud with Limited and Imbalanced Data—0
Mining Artifacts in Mycelium SEM Micrographs—0
Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model—0
Mining Meta-indicators of University Ranking: A Machine Learning Approach Based on SHAP—0
Social Media Information Sharing for Natural Disaster Response—0
Mining Robust Default Configurations for Resource-constrained AutoML—0
MiniVQA - A resource to build your tailored VQA competition—0
Misinformation detection in Luganda-English code-mixed social media text—0
Missing Generalizations: A Supervised Machine Learning Approach to L2 Written Production—0
Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers—0
Mitigating Edge Machine Learning Inference Bottlenecks: An Empirical Study on Accelerating Google Edge Models—0
Mitigating Gender Bias in Machine Learning Data Sets—0
A Machine Learning Model for Predicting, Diagnosing, and Mitigating Health Disparities in Hospital Readmission—0
Mitigation of Adversarial Attacks through Embedded Feature Selection—0
MITRE at SemEval-2017 Task 1: Simple Semantic Similarity—0
Mixed Formal Learning: A Path to Transparent Machine Learning—0
MixML: A Unified Analysis of Weakly Consistent Parallel Learning—0
Mixtures of Gaussian Processes for regression under multiple prior distributions—0
Why Mixup Improves the Model Performance—0
ML4CO: Is GCNN All You Need? Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems, If Tuned and Trained Properly, on Appropriate Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100—Unverified