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 2576–2600 of 3073 papers

TitleStatusHype
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian ModelCode0
Active Decision Boundary Annotation with Deep Generative ModelsCode0
Adaptive Open-Set Active Learning with Distance-Based Out-of-Distribution Detection for Robust Task-Oriented Dialog SystemCode0
ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument AggregationCode0
Identifying Adversarially Attackable and Robust SamplesCode0
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language ModelsCode0
Deep Active Learning for Anchor User PredictionCode0
Computational Assessment of Hyperpartisanship in News TitlesCode0
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical GuaranteesCode0
Textual Membership QueriesCode0
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference LandscapesCode0
Image-based Detection of Surface Defects in Concrete during ConstructionCode0
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functionsCode0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
State-Relabeling Adversarial Active LearningCode0
Committee neural network potentials control generalization errors and enable active learningCode0
ImitAL: Learned Active Learning Strategy on Synthetic DataCode0
ImitAL: Learning Active Learning Strategies from Synthetic DataCode0
Active Gradual Machine Learning for Entity ResolutionCode0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
Model Transfer for Tagging Low-resource Languages using a Bilingual DictionaryCode0
The Battleship Approach to the Low Resource Entity Matching ProblemCode0
Active Collaborative Sensing for Energy BreakdownCode0
Active Learning for Visual Question Answering: An Empirical StudyCode0
Combining MixMatch and Active Learning for Better Accuracy with Fewer LabelsCode0
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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