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BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

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Showing 701–725 of 10033 papers

TitleStatusHype
Case study: Mapping potential informal settlements areas in Tegucigalpa with machine learning to plan ground surveyCode1
Modern Methods for Text GenerationCode1
Characterizing metastable states with the help of machine learningCode1
Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning WorkloadsCode1
Multi-class Gaussian Process Classification with Noisy InputsCode1
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
Multitask learning and benchmarking with clinical time series dataCode1
Naive Automated Machine LearningCode1
NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine LearningCode1
CLAUDETTE: an Automated Detector of Potentially Unfair Clauses in Online Terms of ServiceCode1
NetML: A Challenge for Network Traffic AnalyticsCode1
Climate-Invariant Machine LearningCode1
Clinica: an open source software platform for reproducible clinical neuroscience studiesCode1
Coarse Graining Molecular Dynamics with Graph Neural NetworksCode1
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization SolversCode1
Neural-PDE: A RNN based neural network for solving time dependent PDEsCode1
Neuroevolution of Neural Network Architectures Using CoDeepNEAT and KerasCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and GenerationCode1
Nonlinear Least Squares for Large-Scale Machine Learning using Stochastic Jacobian EstimatesCode1
Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance ComputingCode1
Adversarial Examples Are Not Bugs, They Are FeaturesCode1
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamicsCode1
A Model-Driven Approach to Machine Learning and Software Modeling for the IoTCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100—Unverified