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BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

Showing 726750 of 10033 papers

TitleStatusHype
Proposed Guidelines for the Responsible Use of Explainable Machine LearningCode1
Gradio: Hassle-Free Sharing and Testing of ML Models in the WildCode1
GIBBONFINDR: An R package for the detection and classification of acoustic signalsCode1
Coresets for Data-efficient Training of Machine Learning ModelsCode1
Risks from Learned Optimization in Advanced Machine Learning SystemsCode1
Meta Dropout: Learning to Perturb Features for GeneralizationCode1
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object TrackingCode1
Adversarial Examples Are Not Bugs, They Are FeaturesCode1
Convolutional neural networks: a magic bullet for gravitational-wave detection?Code1
Text Classification Algorithms: A SurveyCode1
CryptoNN: Training Neural Networks over Encrypted DataCode1
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc InterpretabilityCode1
Data Shapley: Equitable Valuation of Data for Machine LearningCode1
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning SystemsCode1
Augmented Ultrasonic Data for Machine LearningCode1
Learning Fast Algorithms for Linear Transforms Using Butterfly FactorizationsCode1
Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product ManipulationCode1
GNNExplainer: Generating Explanations for Graph Neural NetworksCode1
Evaluating Differentially Private Machine Learning in PracticeCode1
ParticleNet: Jet Tagging via Particle CloudsCode1
Towards Federated Learning at Scale: System DesignCode1
ProteinNet: a standardized data set for machine learning of protein structureCode1
Interpretable machine learning: definitions, methods, and applicationsCode1
A Machine Learning Benchmark for Facies ClassificationCode1
A Comprehensive Survey on Graph Neural NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100Unverified