SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Showing 641650 of 3073 papers

TitleStatusHype
Relevance feedback strategies for recall-oriented neural information retrieval0
A unified framework for learning with nonlinear model classes from arbitrary linear samples0
Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning0
Multi-Objective Bayesian Optimization with Active Preference Learning0
Evidential Uncertainty Quantification: A Variance-Based PerspectiveCode1
Active Prompt Learning in Vision Language ModelsCode1
RONAALP: Reduced-Order Nonlinear Approximation with Active Learning Procedure0
FOCAL: A Cost-Aware Video Dataset for Active LearningCode0
Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks0
Correlation-aware active learning for surgery video segmentation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified