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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 576–600 of 10033 papers

TitleStatusHype
Testing robustness of predictions of trained classifiers against naturally occurring perturbations—0
A Learned Index for Exact Similarity Search in Metric Spaces—0
Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space—0
Assessing Machine Learning Algorithms for Near-Real Time Bus Ridership Prediction During Extreme Weather—0
A Brief Guide to Designing and Evaluating Human-Centered Interactive Machine Learning—0
Backdooring Explainable Machine Learning—0
Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)—0
Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnosticsCode0
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting—0
Sampling Strategies for Static Powergrid Models—0
Sintel: A Machine Learning Framework to Extract Insights from SignalsCode3
Exploration of Machine Learning Classification Models Used for Behavioral Biometrics Authentication—0
Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine LearningCode0
Software Engineering Approaches for TinyML based IoT Embedded Vision: A Systematic Literature Review—0
On Parametric Optimal Execution and Machine Learning SurrogatesCode1
Table Enrichment System for Machine Learning—0
Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey—0
Trinary Tools for Continuously Valued Binary Classifiers—0
AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks—0
Synthetic Distracted Driving (SynDD2) dataset for analyzing distracted behaviors and various gaze zones of a driverCode0
Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake SystemCode1
Simulation of machine learning-based 6G systems in virtual worlds—0
Characterizing metastable states with the help of machine learningCode1
Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts—0
An interpretable machine learning approach for ferroalloys consumptions—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100—Unverified