SOTAVerified

BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

Showing 476500 of 10033 papers

TitleStatusHype
Machine learning on knowledge graphs for context-aware security monitoringCode1
Challenges in Representation Learning: A report on three machine learning contestsCode1
Characterizing metastable states with the help of machine learningCode1
Coresets for Data-efficient Training of Machine Learning ModelsCode1
Making a Spiking Net Work: Robust brain-like unsupervised machine learningCode1
CLAUDETTE: an Automated Detector of Potentially Unfair Clauses in Online Terms of ServiceCode1
Malware Classification Using Static Disassembly and Machine LearningCode1
Manthan: A Data Driven Approach for Boolean Function SynthesisCode1
ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-based Type InferenceCode1
Markpainting: Adversarial Machine Learning meets InpaintingCode1
Clinica: an open source software platform for reproducible clinical neuroscience studiesCode1
Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning WorkloadsCode1
Dataset Optimization Strategies for MalwareTraffic DetectionCode1
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
AGNet: Weighing Black Holes with Machine LearningCode1
A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield PredictionCode1
MementoML: Performance of selected machine learning algorithm configurations on OpenML100 datasetsCode1
Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning StudyCode1
A graph representation of molecular ensembles for polymer property predictionCode1
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and GenerationCode1
MEUZZ: Smart Seed Scheduling for Hybrid FuzzingCode1
Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance ComputingCode1
Mixed-Integer Optimization with Constraint LearningCode1
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamicsCode1
Leakage of Dataset Properties in Multi-Party Machine LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100Unverified