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 24812490 of 3073 papers

TitleStatusHype
Neural Window Decoder for SC-LDPC Codes0
NeuroADDA: Active Discriminative Domain Adaptation in Connectomic0
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning0
NIL\_UCM: Extracting Drug-Drug interactions from text through combination of sequence and tree kernels0
Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Condition0
Noise-tolerant, Reliable Active Classification with Comparison Queries0
Noisy Generalized Binary Search0
Cooperative Inverse Reinforcement LearningCode0
Merging Weak and Active Supervision for Semantic ParsingCode0
RIM: Reliable Influence-based Active Learning on GraphsCode0
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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