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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2476–2500 of 3073 papers

TitleStatusHype
NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials—0
Neural Active Learning Beyond Bandits—0
Neural Active Learning Meets the Partial Monitoring Framework—0
Neural Active Learning with Performance Guarantees—0
Neural Network-Based Active Learning in Multivariate Calibration—0
Neural Window Decoder for SC-LDPC Codes—0
NeuroADDA: Active Discriminative Domain Adaptation in Connectomic—0
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning—0
NIL\_UCM: Extracting Drug-Drug interactions from text through combination of sequence and tree kernels—0
Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Condition—0
Noise-tolerant, Reliable Active Classification with Comparison Queries—0
Noisy Generalized Binary Search—0
Cooperative Inverse Reinforcement LearningCode0
Merging Weak and Active Supervision for Semantic ParsingCode0
RIM: Reliable Influence-based Active Learning on GraphsCode0
RISAN: Robust Instance Specific Abstention NetworkCode0
HC4: A New Suite of Test Collections for Ad Hoc CLIRCode0
Correlation Clustering with Adaptive Similarity QueriesCode0
Risk-Aware Active Inverse Reinforcement LearningCode0
Cost-Accuracy Aware Adaptive Labeling for Active LearningCode0
A3: Active Adversarial Alignment for Source-Free Domain AdaptationCode0
Partition-Based Active Learning for Graph Neural NetworksCode0
Cost-Effective Active Learning for Deep Image ClassificationCode0
Cost-Effective Active Learning for Melanoma SegmentationCode0
Cost Effective Active SearchCode0
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Benchmark Results

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