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 2351–2375 of 3073 papers

TitleStatusHype
Discriminative Active LearningCode0
Self-Regulated Interactive Sequence-to-Sequence LearningCode0
The Power of Comparisons for Actively Learning Linear Classifiers—0
A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images—0
AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging—0
Learning How to Active Learn by DreamingCode0
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
L*-Based Learning of Markov Decision Processes (Extended Version)—0
The Practical Challenges of Active Learning: Lessons Learned from Live Experimentation—0
Deep Active Learning with Adaptive AcquisitionCode0
'In-Between' Uncertainty in Bayesian Neural Networks—0
Selection via Proxy: Efficient Data Selection for Deep LearningCode0
A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree—0
Active Learning Solution on Distributed Edge Computing—0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
Flattening a Hierarchical Clustering through Active Learning—0
Regional based query in graph active learningCode0
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study—0
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active LearningCode0
Batch Active Learning Using Determinantal Point ProcessesCode0
Deep Active Learning for Anchor User PredictionCode0
RadGrad: Active learning with loss gradients—0
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation—0
Low-resource Deep Entity Resolution with Transfer and Active Learning—0
Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques—0
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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