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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 551–575 of 10033 papers

TitleStatusHype
Adaptive Risk Minimization: Learning to Adapt to Domain ShiftCode1
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort studyCode1
A Novel Framework for Spatio-Temporal Prediction of Environmental Data Using Deep LearningCode1
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational DataCode1
Apache Submarine: A Unified Machine Learning Platform Made SimpleCode1
A Non-Parametric Test to Detect Data-Copying in Generative ModelsCode1
Bayesian Tensor Network with Polynomial Complexity for Probabilistic Machine LearningCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Anomaly Detection in Emails using Machine Learning and Header InformationCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
Bias in Machine Learning Software: Why? How? What to do?Code1
BIKED: A Dataset for Computational Bicycle Design with Machine Learning BenchmarksCode1
Brain Predictability toolbox: a Python library for neuroimaging based machine learningCode1
Bridging the gap to real-world for network intrusion detection systems with data-centric approachCode1
Canonicalizing Knowledge Base LiteralsCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
A Bayesian neural network predicts the dissolution of compact planetary systemsCode1
Challenges in Representation Learning: A report on three machine learning contestsCode1
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural NetworksCode1
CLAUDETTE: an Automated Detector of Potentially Unfair Clauses in Online Terms of ServiceCode1
Coarse Graining Molecular Dynamics with Graph Neural NetworksCode1
Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning WorkloadsCode1
Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance ComputingCode1
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamicsCode1
Anonymizing Machine Learning ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100—Unverified