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 29012910 of 3073 papers

TitleStatusHype
Bootstrapping Phrase-based Statistical Machine Translation via WSD Integration0
Boundary Matters: A Bi-Level Active Finetuning Framework0
Bounded Memory Active Learning through Enriched Queries0
Bounds on the Generalization Error in Active Learning0
Breaking Boundaries Between Induction Time and Diagnosis Time Active Information Acquisition0
Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection0
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training0
Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Sampling0
Bayesian Active Learning for Sim-to-Real Robotic Perception0
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation0
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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