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 26512700 of 3073 papers

TitleStatusHype
DeMuX: Data-efficient Multilingual LearningCode0
DenseReviewer: A Screening Prioritisation Tool for Systematic Review based on Dense RetrievalCode0
The Future of Data Science EducationCode0
Multilingual Detection of Personal Employment Status on TwitterCode0
Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital HumanitiesCode0
Sample Efficient Learning of Predictors that Complement HumansCode0
Derivative free optimization via repeated classificationCode0
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement PrioritizationCode0
A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TARCode0
Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation ModelsCode0
Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait SketchingCode0
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
Detecting Minority Arguments for Mutual Understanding: A Moderation Tool for the Online Climate Change DebateCode0
STONE: A Submodular Optimization Framework for Active 3D Object DetectionCode0
Sample Noise Impact on Active LearningCode0
Active Structure Learning of Bayesian Networks in an Observational SettingCode0
Detecting value-expressive text posts in Russian social mediaCode0
Multi-Resolution Active Learning of Fourier Neural OperatorsCode0
Building a comprehensive syntactic and semantic corpus of Chinese clinical textsCode0
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image SegmentationCode0
Multitask Active Learning for Graph Anomaly DetectionCode0
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network modelsCode0
Sampling and Reconstruction of Signals on Product GraphsCode0
Incremental Domain Adaptation for Neural Machine Translation in Low-Resource SettingsCode0
Incremental Robot Learning of New Objects with Fixed Update TimeCode0
Multi-task Active Learning for Pre-trained Transformer-based ModelsCode0
Differentially Private Active Learning: Balancing Effective Data Selection and PrivacyCode0
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based InferenceCode0
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node ClassificationCode0
Inferring solutions of differential equations using noisy multi-fidelity dataCode0
XAL: EXplainable Active Learning Makes Classifiers Better Low-resource LearnersCode0
Architectural and Inferential Inductive Biases For Exchangeable Sequence ModelingCode0
Info-Coevolution: An Efficient Framework for Data Model CoevolutionCode0
APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement LearningCode0
Information Condensing Active LearningCode0
A Practical Incremental Learning Framework For Sparse Entity ExtractionCode0
Active Learning via Classifier Impact and Greedy Selection for Interactive Image RetrievalCode0
Black-Box Batch Active Learning for RegressionCode0
Disambiguation of Company names via Deep Recurrent NetworksCode0
DISCERN: Decoding Systematic Errors in Natural Language for Text ClassifiersCode0
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
Discovering General-Purpose Active Learning StrategiesCode0
Approximate Bayesian Computation with Domain Expert in the LoopCode0
Sampling Bias in Deep Active Classification: An Empirical StudyCode0
Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular SimulationCode0
A-Optimal Active LearningCode0
Bidirectional Uncertainty-Based Active Learning for Open Set AnnotationCode0
Predictive Accuracy-Based Active Learning for Medical Image SegmentationCode0
Discriminative Active LearningCode0
Instance-wise Supervision-level Optimization in Active LearningCode0
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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