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BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

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Showing 9051–9075 of 10033 papers

TitleStatusHype
Machine Learning and Feature Engineering for Predicting Pulse Status during Chest CompressionsCode0
The Unreasonable Effectiveness of Machine Learning in Moldavian versus Romanian Dialect IdentificationCode0
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine LearningCode0
Effective dimension of machine learning modelsCode0
Fuzzy Jaccard Index: A robust comparison of ordered listsCode0
An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy SettingsCode0
Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine LearningCode0
Cardinality Estimation in a Virtualized Network Device Using Online Machine LearningCode0
Attribution of Gradient Based Adversarial Attacks for Reverse Engineering of DeceptionsCode0
Addressing the Fundamental Tension of PCGML with Discriminative LearningCode0
EEGsig: an open-source machine learning-based toolbox for EEG signal processingCode0
Game Plan: What AI can do for Football, and What Football can do for AICode0
Machine Learning and System Identification for Estimation in Physical SystemsCode0
Recognizing Birds from Sound - The 2018 BirdCLEF Baseline SystemCode0
PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approachCode0
Ecole: A Library for Learning Inside MILP SolversCode0
Towards Size-Independent Generalization Bounds for Deep Operator NetsCode0
Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?Code0
Easy Hyperparameter Search Using OptunityCode0
Can We Achieve More with Less? Exploring Data Augmentation for Toxic Comment ClassificationCode0
Gaussian boson sampling and multi-particle event optimization by machine learning in the quantum phase spaceCode0
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models InsteadCode0
Calibrate to InterpretCode0
Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries Across Software Package EcosystemsCode0
Gaussian Process Random FieldsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100—Unverified