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BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

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Showing 461–470 of 10033 papers

TitleStatusHype
Knowledge-Based Learning of Nonlinear Dynamics and ChaosCode1
Learning outside the Black-Box: The pursuit of interpretable modelsCode1
Revisiting local branching with a machine learning lensCode1
Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and GeneralizationCode1
LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural NetworksCode1
Lettuce: PyTorch-based Lattice Boltzmann FrameworkCode1
Lifelong Machine Learning of Functionally Compositional StructuresCode1
LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning TasksCode1
XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series ClassificationCode1
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100—Unverified